The inter-annotator agreement was generally substantial, and annotator performance was adequate, indicating that the annotation scheme produces consistent event annotations of high quality.❿
 
 

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You can travel freely in the day or night in town or in the countryside. The law in Germany allows international students to work part-time for up to 20 hours a week or full days a year. Part-time jobs usually include teaching assistant positions at the university itself to babysitting, tutoring, bartending, retail or administrative jobs. Your work experience may increase your chances of future employability and can teach you to add discipline to your lifestyle and live independently.

A degree from a German university coupled with work experience is not only an asset when applying for a future job in Germany but also valued in the global job market. Students also get to deep dive into the German culture and take time off from their studies, which many international students utilize to find out more about their host country. From going to a museum, a cinema or a theatre, to visiting a beer garden or old castles.

We hope these tips motivate you enough to make the right decision in your foreign education journey. For more information on education in Germany visit uni-access. UK plsHelpUkraine?? The New Yorker plsHelpUkraine?? It also stated that The younger the patient is at the time of recognition and treatment of her primary disease the longer the evidence of metastasis is likely to be postponed clomid for men dosage Interestingly, TAX could alter this force distribution in a concentration dependent manner.

Ukraine fatigue?? Major push?? Czech Republic. United Kingdom. Apply Now. Apr 07, By Digichefs Blog The looming question of, which country is the right place for my educational goals? Write Comment. Leonardbof 21 Dec , CharlesGlide 19 Dec , Woutioums 16 Dec , Robertmom 16 Dec , Glitlygat 15 Dec , Cardirm 11 Dec , Publisher : Springer Cham. Series ISSN : Edition Number : 1. Number of Pages : XXI, Skip to main content.

Search SpringerLink Search. View editor publications. Buying options eBook EUR It provides an industry—recognized and valuable credential that may open doors with prospective employers or lead to job advancement opportunities. This two—course program covers core hardware and operating systems technologies.

Upon completion of this program, you will be able to demonstrate basic knowledge of configuring, installing, diagnosing, repairing, upgrading, and maintaining computers and associated technologies. Topics include installing, building, repairing, configuring, troubleshooting, and preventive maintenance of hardware.

At the end of course, you will build your own personal computer. A list of required tools will be distributed at the first class meeting. Prerequisite: Working knowledge of personal computers. Most companies recognize this certification as the primary skill level for network technicians.

Prerequisite: Basic knowledge of PCs. Topics include installing, building, repairing, configuring, and troubleshooting. Through lectures and labs, you will learn how to install and set up operating systems, configure, troubleshoot, maintain and manage hardware using operating systems. This certification is the worldwide standard for professional network support or administration. Learn to configure and troubleshoot basic networking hardware, protocols and services, as well as network implementation, network support diagnosing and troubleshooting, media topologies, protocols and standards.

This is a hands—on course. This certification is recognized around the world as the first level of computer security competence. A basic networking course is recommended. You will utilize state—of—the—art network simulators for hands—on practice during supervised lab sessions. Learn and acquire hands—on skills in a real network environment to configure, operate, and troubleshoot routed and switched networks and prepare for CCNA certification.

Cisco routers are widely deployed in enterprises and the Internet to support the network infrastructure. Anyone interested in networking must become familiar with Cisco routers and IOS software.

Taught by an experienced network engineer with multiple certifications, this course combines lecture and hands—on labs to introduce the following topics: router basics, router architecture and hardware components, Cisco router IOS software, IP routing protocols, configuration and troubleshooting, router user interface, and user and privileged mode commands. Through lectures and hands—on labs and exercises, this course teaches you to configure, operate and maintain LAN switches.

The course also covers the Cisco switching overview, switching family architecture, protocols and software, configuring Catalyst series switches and VLANs, switch CLI commands, menu and web access, and placing switches in the network.

This course introduces network security concepts, technologies, and implementation to participants. Hands—on labs for ACL configuration are included.

Wireless Networks are quickly becoming commonplace in the office, home, school, and many other locations. WLAN topology, architecture and security are also introduced to participants. Since this is the last class in this certificate program, additional topics such as IPv6, reviews and practice tests for CCNA preparation will also be covered. Networking Fundamentals This course introduces networking technologies, concepts and capabilities.

Taught by a professional civil engineer, and designed for both beginners and students with some AutoCAD experience, this introductory course focuses on the basic operation and application of the AutoCAD software.

A wide variety of commands and applications will be covered. Hands—on experience in a supervised CAD lab is included in the instruction to increase precision, efficiency and productivity. It then focuses on the methods used to modify and refine geometry while emphasizing accuracy and good habits to build a solid design foundation.

Comprehensive hands— on tutorials are used for you to complete fun, competency—based project exercises to further enhance your learning in AutoCAD. Materials provided in class. AutoCAD Level II introduces some of the more intermediate topics used to produce quality complex engineering drawings. You will learn to increase efficiency and productivity by exploring topics that include advanced object types, advanced editing commands, advanced blocks, external references, dimensioning, and plotting.

Comprehensive hands—on tutorials are used for you to complete fun, competency—based project exercises to further enhance your learning in AutoCAD. This hands—on course is an introduction into computer—aided drafting CAD and is designed for you to learn the basic commands to create and edit 2—D drawings.

This course is beneficial for all types of drafting and the skills learned can be applied to any field that requires CAD drawings. Explore the application of Revit to architectural drafting, and use it to produce preliminary architectural drawings. Learn about plans, sections, elevations, and schedules through hands—on projects to gain practical experience. Prerequisites: An understanding of Architectural Drafting.

CAD experience is helpful but not required. The Java Programming Certificate will assist you in acquiring a solid, practical knowledge base and prepare you for a wide range of IT careers. This 65—hour program is specifically tailored to go beyond basic concepts and skills in working with the Java programming language, which is universally accepted as the candidate programming language for developing web application systems. Individual courses may be taken without pursuing the program certificate.

This fast—paced course focuses on basic object—oriented programming methodology. Programming experience is helpful. Textbook: Java: How to Program, 9th Ed. Intermediate Java Programming This fast—paced course focuses on intermediate, object—oriented programming methodology, expanding on and reinforcing basic concepts learned in the introductory course. Advanced Java Programming Move your knowledge to the advanced level by learning about more swing GUI components and get an explanation of the concept of event—driven programming.

His instructions are very clear and his passion for teaching really shows. I would love to take more classes with him. Very willing to answer questions and provides accurate answers. Multivision, an IT services company with over employees, was ranked 2nd fastest growing IT consulting firm in the Washington, DC metro area by Inc.

Classes are held at Multivision, located at Fairfax Blvd. Go to www. Completion of the Database Administrator Certificate is a great addition to your resume. Required courses:. Access — Introduction See page 31 for description. Access — Intermediate See page 31 for description. SQL is the universal and fundamental language for writing queries and manipulating data in Object Relational Databases. All courses may not be offered every semester. Prerequisite: Oracle SQL 11g or equivalent experience.

Creating and managing, new constraints in creating tables and new features from the Object—Relational database in Oracle Version 11g will also be discussed. Oracle Database Administration 11g Learn about identifying the main structure and functionality of the Oracle Engine, Oracle 11g, examining physical file structures that make up an Oracle database, starting up and shutting down, creating an Oracle instance and managing table space and users.

Classical Data export utility and import utility, new data pump export and import utilities, setting up of an audit trail and tuning are also covered. Database cold and hot backup recovery strategy will be discussed in depth and analyzed. Every session includes a lecture followed by hands—on training in all aspects listed above.

Prerequisites: Understanding of basic networking terminology and Windows. Advanced topics such as network security, configuring sites and replication, Group Policy Objects GPOs , and server backup and recovery will also be covered. Understand why every network administrator in the world needs to care about, learn about and use MS PowerShell.

Prerequisite: Experience as a network administrator — XP, Windows or Become fully acquainted with the newest server from Microsoft. You will learn how add, delete, and modify users, organizational units and groups in the Active Directory. All the while, you will be preparing yourself to take the first Server Exam 70— on the way to the MCSA certification.

Learn about the necessary hardware components of the basic LAN: the server, cabling, workstations, and network interface cards. Also, discover the intricacies of establishing advanced complex internetworks and the devices specific to them: switches and routers. The most popular network operating systems will be reviewed along with application software used on LANs. Textbook: Guide to Networking Essentials, 6th Ed.

Windows 7, Configuration 70— Get the skills and knowledge needed to support end users who run Windows 7 Professional Edition in a corporate or small business environment.

Please bring an 8 GB thumb drive to the first class session. CyberWatch Security Get an introduction to the evolving world of network security. Discover what the forces of evil are doing to pry into your network or PC. What are they using to exploit your organization? How can you protect yourself?

Discussions will focus on individual PCs and total network operations. You cannot afford to miss this course! Cyber Incident Handling Get hands—on experience for analysis of security—related incidents on Windows based servers and PCs. Examine MS artifacts and Windows Registry components. Cloud storage is now readily available and easily accessible. It is very secure, storage is free, and it enables you to access files anywhere and at any time. All you need to know is how to take advantage of it.

This instructor—led course will describe the various service delivery models of a cloud computing architecture and the ways in which clouds can be deployed as public, private, hybrid, and community clouds.

This is a hands— on course taught in a lab environment. Building Websites with Drupal One of the must—haves for any small business, be it start—up or long—established, is an insightful website that allows people to find out more about the company. It gives any small business credibility in an increasingly technology—driven world. Learn how to build a website quickly using Drupal, a completely free and open source content management system. Advanced Drupal There are an infinite number of themes and modules available to customize the look and feel of a Drupal website.

Delve into the inner workings of the Drupal theming system and get a better understanding of how to customize and make your Drupal site unique for your audience.

Explore the effective use of the Views modules and theme overriding to change the look and feel of the site. You can use Acrobat Standard or Acrobat Pro to convert virtually any document to Adobe Portable Document Format PDF , preserving the exact look and content of the original, complete with fonts and graphics.

You can edit the text and images in PDF documents, use Acrobat in a review cycle, distribute and share documents, create forms, add security, and more. Many thanks for your excellent knowledge and experience, Faydra.

Looking forward to DW Level 2. You must know how to download and open zip files. Call —— or e—mail clangguth nvcc. Check with your Internet Service Provider. Familiarize yourself with how to add images and where those images will be located.

See website for optional textbook. Photoshop — Level I Photoshop is the graphics program most used by professional graphic designers.

It allows you to manipulate images, create amazing graphics with equally amazing effects. Learn to repair old photos, colorize black and white photos, or freshen up a faded one. This class, although written for CS5, has been updated with the new additions for CS6. Anyone with Photoshop CS and above can benefit from this course. Photoshop — Level II The Photoshop Level II class is designed to continue from where the Photoshop Level I class left off and introduces you to more of the wonderful things you can do with this program.

Armed with the knowledge of how the program works, you can now put that to use with more advanced skills. Prerequisite: Photoshop Level 1 class or equivalent experience. Each lesson has only one of the topics mentioned, so they go much further in depth than previous classes.

Lightroom Organize and manage your photos using a complete workflow solution which allows you to make modifications non— destructively. The work you do to your photos is not actually done on the photo itself. The information is stored in a catalog. This allows you to experiment and try different things without compromising your original photo. Expression Web Create a basic website, assign attributes, hyperlinks and images, and learn document control and placement. Create forms, learn to use predesigned templates and much more.

Tie this all up with publishing your site to the Web. This Web Design Certificate Program is structured for those who wish to become professional web designers.

You must successfully complete the six core courses listed below, plus two electives totaling 24 hours 2.

Individual classes may be taken without pursuing the program certificate. All classes may not be offered every semester. This hands—on course introduces you to the exciting world of web page creation for the Internet using the newly developed HTML5 coding standards.

Topics include links, images, lists and tables. Some exposure to the new CSS3, as well as the new audio and video tags, is also included. The first hour of class is crucial to your success. Please be on time and bring a thumb drive so you can save your work.

Prerequisites: Familiarity with Windows and Notepad. Learn the syntax of the JavaScript language, functions and events, the DOM model, menus, and how to add or modify windows; create, access or modify elements, use animation and graphics, and enter data through forms.

Time permitting, other topics include: objects, date and timing events, and validation. An introduction to jQuery will also be made. Prior exposure to a programming language is helpful. Bring a thumb drive to save your work. Adobe Photoshop is the industry standard for image manipulation and preparation. Graphic images used in print, multimedia, and the internet are often created in Photoshop and then imported into other programs. Get a hands—on introduction to Photoshop, exploring its workspace, tools, palettes, and menu options and discussing their potential uses.

No previous Photoshop or art knowledge is required. Prerequisites: Ability to locate, open, and save files in a Windows environment and be comfortable using a mouse. Topics covered include: new form elements, audio and video, scalable vector graphics, the new page structural elements, plus some additional details.

The QueryString will also be discussed. A brief introduction to JavaScript and jQuery will be made. Bring a thumb drive to class. Prerequisite: Intro to Web Page Design or equivalent course. These lab sessions are totally self—paced and project—based.

The biggest advantage of the lab sessions is having your instructor there to devote time and attention to your specific questions and concerns. Adobe Illustrator software allows you to create sophisticated artwork for virtually any medium.

Industry—standard drawing tools and flexible color controls help you capture your ideas and experiment freely, while timesaving features such as easier— to—access options let you work quickly and intuitively. Improved performance and tight integration with other Adobe applications also help you produce extraordinary graphics.

Prerequisites: Experience with a personal computer. Experience with graphics is helpful, but not required. With Photoshop CS6, you can perfect your photography with breakthrough tools. Photoshop CS6 offers state—of—the—art tools to help you refine your images and get superior results faster than ever before and boost your productivity at every level. New selection technology helps you easily make complex selections of difficult subjects with superior results, and Adobe Mini Bridge keeps your photos and media close to hand—right inside Photoshop.

Prerequisites: Adobe Photoshop Level 1 or equivalent experience. Ability to use the mouse, know standard menus and commands, and also how to open, save, and close files.

Dreamweaver is the number one, third— party web design application on the market today. Thousands of designers use it to create beautiful and dynamic websites for their clients. You can too! Discover how to insert images in the background and foreground of your pages; create tables; use more advanced CSS; create different types of page layouts with CSS sheets; check your pages for browser compatibility; insert JavaScript into web pages, and create forms.

Get on the web design fast track! It is recommended that courses be taken in the order listed above. Individual courses may be taken without pursuing the program certificate, and individual course certificates will be awarded. All courses in this program may not be offered every semester. Arlington Center, Instructor: Dr. Learn to format web pages that adapt from desktop to tablet to phone size using CSS Media Queries as specified in international standards.

Discover how to lay out a page in one, two, or three columns that can collapse or rearrange themselves, and also learn all the basic CSS for formatting the typography, colors, and backgrounds of your pages. Please bring a flash drive so you can save your work. Preparing images and video for the Web, you will create banners and background graphics with appropriate resolution and color space for viewing on screen.

You will edit video to add transitions, still images, and titles. The class will also cover basics of image editing and masking with a focus on new features. Prerequisites: experience working with a personal computer. Please bring an empty thumb drive. Dreamweaver for Effective Web Design Dreamweaver is the tool—of—choice for professionals to create Web sites more quickly than coding by hand. In this fast— paced, hands—on course, you will learn both the strategies of effective web design and the skills for creating sites in Dreamweaver.

You will shortcut the design of mobile—ready pages using starter pages and fluid grids. You will add forms for user input, create templates that permit global changes across a site, and insert interactive media and the new CSS3 transformations. Class will finish with a project in which you create a site of your own design.

Prerequisite: Familiarity with basic computer operations. Learn the best sources of these systems, how to pick the ones that fit your needs, and how to install and customize them——including the popular WordPress, Joomla, Drupal, and more.

Our website is updated frequently. Please check it for the latest course information. Visit: www. This hands—on course introduces you to web page creation for the Internet using the newly developed HTML5 coding standards. Topics include headings, lists, links, images, and tables. Please bring a thumb drive so you can save your work. Web Design Studio Lab Sharpen your Web design skills by building a full site with support from your instructor coach. This lab is for those who have completed the Web Design Certificate program—or have comparable skills—and want to focus on a real—world project.

The instructor will offer support and will assess your completed project. Bring a project of your own or choose one proposed by the instructor. Prerequisites: All classes in the Web Design Certificate or equivalent experience.

Learn data types, expressions, conditional and looping statements, arrays, functions and event handlers. You will learn how to write JavaScript to create menus, access information from other web pages, dynamically modify CSS, and utilize dates and time. Audio and video can work across all browsers. Forms look sharper and come alive with built in validation.

New page elements produce content that is portable from your site to Req. Textbook: Modern JavaScript Develop and social media. This program is designed for individuals who want to gain control for any beginning and advanced website development. The program also includes an overview course on basic web management to enable you to learn how to actually publish a small or medium size website in a shared hosting environment.

It is ideal for individuals who wish to create sites for small business, non—profit or personal use or as a foundational lead—in to a professional development career with a large business. You may choose to complete the Level I skills certificate only.

Teri Murphy created her first Web site as a volunteer while she was vacationing in the Bahamas. She offered to create a site for a bakery popular with tourists. The baker parlayed the site into a highly successful business of selling beach houses, and he hired Teri as his permanent Web master, requiring her to return each year.

Teri encourages her students to start practicing their new skills immediately by volunteering in a similar manner, and thereby building a portfolio that can lead to freelance clients or a corporate job. No special software is required. You will enhance the pages using Cascading Style Sheets CSS to add color, fonts, and many other special visual effects.

You may select one of two tracks: Multimedia Design or Web Design. You will also learn how to embed JavaScript in your HTML pages, create rollover images, add form validation, and more. Prerequisite: Web Basics course or equivalent knowledge.

To earn the Web Design Track certificate, you must complete 8 required courses and 24 hours of elective courses. Upon completion of this certificate program you must submit a final project prior to receiving your overall program certificate. You should have basic knowledge in using either a Macintosh or Windows—based computer. The computer system used in a specific course will depend on lab availability. Introduction to Web Design Want to learn how to create Flash animations, place them on a Web page, and then create a dynamic website?

This comprehensive overview course introduces the fundamental techniques and principles involved in the planning, design, and production of web—based designs. Learn how to plan, design, and produce creative, interactive materials for the web. Expand your JavaScript knowledge with special JavaScript frameworks.

This course uses the jQuery Framework for JavaScript. Prerequisite: JavaScript Basics or equivalent knowledge. Get a solid foundation in general computer graphics principles and concepts. Essential concepts for both the web and print mediums are covered, including raster versus vector, resolution, color depth, color models, color management, compression, file formats, pixels, resolution, font properties, optimization, anti—aliasing, and half—toning.

If you are new to computer graphics, this course prepares you to use the software more quickly and efficiently, and is highly recommended before you take Illustrator and Photoshop classes. If you have some experience using computer graphics software but have questions about terms and basic concepts, this course answers them for you.

Adobe Photoshop is the imaging industry standard for image manipulation and preparation. This class provides a hands—on introduction to Photoshop, exploring its workspace, tools, palettes, and menu options and discussing their potential uses. Prerequisite: Ability to locate, open, and save files in a Windows environment and be comfortable using a mouse. In this introduction to Dreamweaver CS6, you harness the power of this professional tool that is the industry standard for creating web pages.

You will also explore basic formatting of web pages, implementing cascading style sheets, and creating dynamic forms. Adobe Illustrator — Level I Learn how to use the popular graphic design application Adobe Illustrator to create graphics and artwork to be used for the web, multimedia, and print. Topics covered include: when to use Illustrator vs. If you are new to computer graphics, the Introduction to Computer Graphics class is highly recommended before you take this class.

You will explore Dreamweaver templates, JavaScript, database—driven pages, and website project management. This course covers the basics in creating, animating, and distributing Flash projects. Topics include: the Flash interface; creating simple to complex animations; creating interactive Flash movies; and integrating sound and video into Flash projects.

Advanced Web Design for Designers This is an independent study, instructor— assisted course. Students will be required to build a website from start to finish by following specific procedures. Students will proceed at their own pace. Completion of this final project is required for the Multimedia Web Design Certification. Prerequisites: All courses in certification program plus 24 hours of electives. When compared to established data augmentation methods, it is substantially more computationally efficient and requires no manual annotation by a human expert as they usually do.

In order to increase its efficiency, we combine acda with two learning optimization techniques: contrastive learning and a hybrid loss function. The former maximizes the benefit of the supervisory signal generated by acda, while the latter incentivises the model to learn the nuances of the decision boundary. Our combined approach is shown experimentally to provide an effective way for mitigating spurious data correlations within a dataset, called dataset artifacts, and as a result improves performance.

Specifically, our experiments verify that acda-boosted pre-trained language models that employ our learning optimization techniques, consistently outperform the respective fine-tuned baseline pre-trained language models across both benchmark datasets and adversarial examples.

Skill Classification SC is the task of classifying job competences from job postings. This work is the first in SC applied to Danish job vacancy data. We study two setups: The zero-shot and few-shot classification setting. Our results show RemBERT significantly outperforms all other models in both the zero-shot and the few-shot setting. Legal texts are often difficult to interpret, and people who interpret them need to make choices about the interpretation.

To improve transparency, the interpretation of a legal text can be made explicit by formalising it. However, creating formalised representations of legal texts manually is quite labour-intensive. In this paper, we describe a method to extract structured representations in the Flint language van Doesburg and van Engers, from natural language. Automated extraction of knowledge representation not only makes the interpretation and modelling efforts more efficient, it also contributes to reducing inter-coder dependencies.

The Flint language offers a formal model that enables the interpretation of legal text by describing the norms in these texts as acts, facts and duties. To extract the components of a Flint representation, we use a rule-based method and a transformer-based method.

In the transformer-based method we fine-tune the last layer with annotated legal texts. This indicates that the transformer-based method is a promising approach of automatically extracting Flint frames. Spelling correction utilities have become commonplace during the writing process, however, many spelling correction utilities suffer due to the size and quality of dictionaries available to aid correction.

Many terms, acronyms, and morphological variations of terms are often missing, leaving potential spelling errors unidentified and potentially uncorrected. This research describes the implementation of WikiSpell, a dynamic spelling correction tool that relies on the Wikipedia dataset search API functionality as the sole source of knowledge to aid misspelled term identification and automatic replacement. Instead of a traditional matching process to select candidate replacement terms, the replacement process is treated as a natural language information retrieval process harnessing wildcard string matching and search result statistics.

The aims of this research include: 1 the implementation of a spelling correction algorithm that utilizes the wildcard operators in the Wikipedia dataset search API, 2 a review of the current spell correction tools and approaches being utilized, and 3 testing and validation of the developed algorithm against the benchmark spelling correction tool, Hunspell.

The key contribution of this research is a robust, dynamic information retrieval-based spelling correction algorithm that does not require prior training. Results of this research show that the proposed spelling correction algorithm, WikiSpell, achieved comparable results to an industry-standard spelling correction algorithm, Hunspell. It is the first of its kind for Commodity News and serves to contribute towards resource building for economic and financial text mining.

This paper describes the data collection process, the annotation methodology, and the event typology used in producing the corpus. Firstly, a seed set of news articles were manually annotated, of which a subset of 25 news was used as the adjudicated reference test set for inter-annotator and system evaluation. The inter-annotator agreement was generally substantial, and annotator performance was adequate, indicating that the annotation scheme produces consistent event annotations of high quality.

Subsequently, the dataset is expanded through 1 data augmentation and 2 Human-in-the-loop active learning. The resulting corpus has news articles with approximately 11k events annotated.

As part of the active learning process, the corpus was used to train basic event extraction models for machine labeling; the resulting models also serve as a validation or as a pilot study demonstrating the use of the corpus in machine learning purposes.

To cope with the COVID pandemic, many jurisdictions have introduced new or altered existing legislation. Even though these new rules are often communicated to the public in news articles, it remains challenging for laypersons to learn about what is currently allowed or forbidden since news articles typically do not reference underlying laws.

We investigate an automated approach to extract legal claims from news articles and to match the claims with their corresponding applicable laws. For both tasks, we create and make publicly available the data sets and report the results of initial experiments. We obtain promising results with Transformer-based models that achieve Furthermore, we discuss challenges of current machine learning approaches for legal language processing and their ability for complex legal reasoning tasks.

Argumentation mining is a growing area of research and has several interesting practical applications of mining legal arguments. Support and Attack relations are the backbone of any legal argument. However, there is no publicly available dataset of these relations in the context of legal arguments expressed in court judgements. In this paper, we focus on automatically constructing such a dataset of Support and Attack relations between sentences in a court judgment with reasonable accuracy.

We propose three sets of rules based on linguistic knowledge and distant supervision to identify such relations from Indian Supreme Court judgments. The first rule set is based on multiple discourse connectors, the second rule set is based on common semantic structures between argumentative sentences in a close neighbourhood, and the third rule set uses the information about the source of the argument.

We also explore a BERT-based sentence pair classification model which is trained on this dataset. We release the dataset of sentence pairs – Support precision We believe that this dataset and the ideas explored in designing the linguistic rules and will boost the argumentation mining research for legal arguments. It contains more than one million tokens with annotation covering three classes: person, location, and organization. The dataset around K tokens mostly contains manual gold annotations in three different domains news, literature, and political discourses and a semi-automatically annotated part.

The multi-domain feature is the main strength of the present work, offering a resource which covers different styles and language uses, as well as the largest Italian NER dataset with manual gold annotations. It represents an important resource for the training of NER systems in Italian. Texts and annotations are freely downloadable from the Github repository. Question answering QA is one of the most common NLP tasks that relates to named entity recognition, fact extraction, semantic search and some other fields.

In industry, it is much valued in chat-bots and corporate information systems. It is also a challenging task that attracted the attention of a very general audience at the quiz show Jeopardy! In this article we describe a Jeopardy! The data set includes , quiz-like questions with 29, from the Russian analogue of Jeopardy! Own Game. We observe its linguistic features and the related QA-task.

We conclude about perspectives of a QA challenge based on the collected data set. In this paper, we present a new corpus of clickbait articles annotated by university students along with a corresponding shared task: clickbait articles use a headline or teaser that hides information from the reader to make them curious to open the article.

We therefore propose to construct approaches that can automatically extract the relevant information from such an article, which we call clickbait resolving. We show why solving this task might be relevant for end users, and why clickbait can probably not be defeated with clickbait detection alone.

Additionally, we argue that this task, although similar to question answering and some automatic summarization approaches, needs to be tackled with specialized models. We analyze the performance of some basic approaches on this task and show that models fine-tuned on our data can outperform general question answering models, while providing a systematic approach to evaluate the results.

We hope that the data set and the task will help in giving users tools to counter clickbait in the future. VALET departs from legacy approaches predicated on cascading finite-state transducers, instead offering direct support for mixing heterogeneous information—lexical, orthographic, syntactic, corpus-analytic—in a succinct syntax that supports context-free idioms. We show how a handful of rules suffices to implement sophisticated matching, and describe a user interface that facilitates exploration for development and maintenance of rule sets.

Arguing that rule-based information extraction is an important methodology early in the development cycle, we describe an experiment in which a VALET model is used to annotate examples for a machine learning extraction model.

While learning to emulate the extraction rules, the resulting model generalizes them, recognizing valid extraction targets the rules failed to detect. Proper recognition and interpretation of negation signals in text or communication is crucial for any form of full natural language understanding. It is also essential for computational approaches to natural language processing. In this study we focus on negation detection in Dutch spoken human-computer conversations.

Since there exists no Dutch dialogue corpus annotated for negation we have annotated a Dutch corpus sample to evaluate our method for automatic negation detection.

Our results show that adding in-domain training material improves the results. We show that we can detect both negation cues and scope in Dutch dialogues with high precision and recall. We provide a detailed error analysis and discuss the effects of cross-lingual and cross-domain transfer learning on automatic negation detection. The Linguistic Data Consortium was founded in to solve the problem that limitations in access to shareable data was impeding progress in Human Language Technology research and development.

At the time, DARPA had adopted the common task research management paradigm to impose additional rigor on their programs by also providing shared objectives, data and evaluation methods. Early successes underscored the promise of this paradigm but also the need for a standing infrastructure to host and distribute the shared data. An open question for the center would be its role in other kinds of research beyond data development. Over its 30 years history, LDC has performed multiple roles ranging from neutral, independent data provider to multisite programs, to creator of exploratory data in tight collaboration with system developers, to research group focused on data intensive investigations.

Through their consistent involvement in EU-funded projects, ELRA and ELDA have contributed to improve the access to multilingual information in the context of the pandemic, develop tools for the de-identification of texts in the legal and medical domains, support the EU eTranslation Machine Translation system, and set up a European platform providing access to both resources and services.

Ethical issues in Language Resources and Language Technology are often invoked, but rarely discussed. This is at least partly because little work has been done to systematize ethical issues and principles applicable in the fields of Language Resources and Language Technology. This paper provides an overview of ethical issues that arise at different stages of Language Resources and Language Technology development, from the conception phase through the construction phase to the use phase. Based on this overview, the authors propose a tentative taxonomy of ethical issues in Language Resources and Language Technology, built around five principles: Privacy, Property, Equality, Transparency and Freedom.

The authors hope that this tentative taxonomy will facilitate ethical assessment of projects in the field of Language Resources and Language Technology, and structure the discussion on ethical issues in this domain, which may eventually lead to the adoption of a universally accepted Code of Ethics of the Language Resources and Language Technology community.

Firstly, in a contrastive manner, by considering two major international conferences, LREC and ACL, and secondly, in a diachronic manner, by inspecting nearly 14, articles over a period of time ranging from to for LREC and from to for ACL. For this purpose, we created a corpus from LREC and ACL articles from the above-mentioned periods, from which we manually annotated nearly 1, We then developed two classifiers to automatically annotate the rest of the corpus. Interestingly, over the considered periods, the results appear to be stable for the two conferences, even though a rebound in ACL could be a sign of the influence of the blog post about the BenderRule.

While aspect-based sentiment analysis of user-generated content has received a lot of attention in the past years, emotion detection at the aspect level has been relatively unexplored. Moreover, given the rise of more visual content on social media platforms, we want to meet the ever-growing share of multimodal content. Additionally, we take the first steps in investigating the utility of multimodal coreference resolution in an ABEA framework.

The presented dataset consists of 4, comments on images and is annotated with aspect and emotion categories and the emotional dimensions of valence and arousal. Our preliminary experiments suggest that ABEA does not benefit from multimodal coreference resolution, and that aspect and emotion classification only requires textual information.

However, when more specific information about the aspects is desired, image recognition could be essential. Sentiment analysis is one of the most widely studied tasks in natural language processing. While BERT-based models have achieved state-of-the-art results in this task, little attention has been given to its performance variability across class labels, multi-source and multi-domain corpora. In this paper, we present an improved state-of-the-art and comparatively evaluate BERT-based models for sentiment analysis on Italian corpora.

The proposed model is evaluated over eight sentiment analysis corpora from different domains social media, finance, e-commerce, health, travel and sources Twitter, YouTube, Facebook, Amazon, Tripadvisor, Opera and Personal Healthcare Agent on the prediction of positive, negative and neutral classes.

Our findings suggest that BERT-based models are confident in predicting positive and negative examples but not as much with neutral examples. We release the sentiment analysis model as well as a newly financial domain sentiment corpus.

Sentiment analysis is one of the most widely studied applications in NLP, but most work focuses on languages with large amounts of data. We propose text collection, filtering, processing and labeling methods that enable us to create datasets for these low-resource languages. We evaluate a range of pre-trained models and transfer strategies on the dataset.

We find that language-specific models and language-adaptive fine-tuning generally perform best. We release the datasets, trained models, sentiment lexicons, and code to incentivize research on sentiment analysis in under-represented languages.

This paper presents a scheme for emotion annotation and its manual application on a genre-diverse corpus of texts written in French. The methodology introduced here emphasizes the necessity of clarifying the main concepts implied by the analysis of emotions as they are expressed in texts, before conducting a manual annotation campaign.

After explaining whatentails a deeply linguistic perspective on emotion expression modeling, we present a few NLP works that share some common points with this perspective and meticulously compare our approach with them.

We then highlight some interesting quantitative results observed on our annotated corpus. The most notable interactions are on the one hand between emotion expression modes and genres of texts, and on the other hand between emotion expression modes and emotional categories.

These observation corroborate and clarify some of the results already mentioned in other NLP works on emotion annotation. In this paper we address the question of how to integrate grammar and lexical-semantic knowledge within a single and homogeneous knowledge graph. We introduce a graph modelling of grammar knowledge which enables its merging with a lexical-semantic network. Such an integrated representation is expected, for instance, to provide new material for language-related graph embeddings in order to model interactions between Syntax and Semantics.

Our base model relies on a phrase structure grammar. The phrase structure is accounted for by both a Proof-Theoretical representation, through a Context-Free Grammar, and a Model-Theoretical one, through a constraint-based grammar.

The constraint types colour the grammar layer with syntactic relationships such as Immediate Dominance, Linear Precedence, and more.

We detail a creation process which infers the grammar layer from a corpus annotated in constituency and integrates it with a lexical-semantic network through a shared POS tagset. We implement the process, and experiment with the French Treebank and the JeuxDeMots lexical-semantic network.

State-of-the-art approaches for metaphor detection compare their literal – or core – meaning and their contextual meaning using metaphor classifiers based on neural networks. However, metaphorical expressions evolve over time due to various reasons, such as cultural and societal impact. Metaphorical expressions are known to co-evolve with language and literal word meanings, and even drive, to some extent, this evolution.

This poses the question of whether different, possibly time-specific, representations of literal meanings may impact the metaphor detection task. To the best of our knowledge, this is the first study that examines the metaphor detection task with a detailed exploratory analysis where different temporal and static word embeddings are used to account for different representations of literal meanings.

Our experimental analysis is based on three popular benchmarks used for metaphor detection and word embeddings extracted from different corpora and temporally aligned using different state-of-the-art approaches. The results suggest that the usage of different static word embedding methods does impact the metaphor detection task and some temporal word embeddings slightly outperform static methods.

However, the results also suggest that temporal word embeddings may provide representations of the core meaning of the metaphor even too close to their contextual meaning, thus confusing the classifier. Overall, the interaction between temporal language evolution and metaphor detection appears tiny in the benchmark datasets used in our experiments.

This suggests that future work for the computational analysis of this important linguistic phenomenon should first start by creating a new dataset where this interaction is better represented. Task embeddings are low-dimensional representations that are trained to capture task properties.

We fit a single transformer to all MetaEval tasks jointly while conditioning it on learned embeddings. The resulting task embeddings enable a novel analysis of the space of tasks.

We then show that task aspects can be mapped to task embeddings for new tasks without using any annotated examples. Predicted embeddings can modulate the encoder for zero-shot inference and outperform a zero-shot baseline on GLUE tasks.

The provided multitask setup can function as a benchmark for future transfer learning research. Automatic Term Extraction ATE is a key component for domain knowledge understanding and an important basis for further natural language processing applications.

Even with persistent improvements, ATE still exhibits weak results exacerbated by small training data inherent to specialized domain corpora. However, no systematic evaluation of ATE has been conducted so far. Experiments have been conducted on four specialized domains in three languages. The obtained results suggest that BERT can capture cross-domain and cross-lingual terminologically-marked contexts shared by terms, opening a new design-pattern for ATE.

We approach aspect-based argument mining as a supervised machine learning task to classify arguments into semantically coherent groups referring to the same defined aspect categories. As an exemplary use case, we introduce the Argument Aspect Corpus – Nuclear Energy that separates arguments about the topic of nuclear energy into nine major aspects.

Since the collection of training data for further aspects and topics is costly, we investigate the potential for current transformer-based few-shot learning approaches to accurately classify argument aspects. The best approach is applied to a British newspaper corpus covering the debate on nuclear energy over the past 21 years. Our evaluation shows that a stable prediction of shares of argument aspects in this debate is feasible with 50 to training samples per aspect.

Moreover, we see signals for a clear shift in the public discourse in favor of nuclear energy in recent years. This revelation of changing patterns of pro and contra arguments related to certain aspects over time demonstrates the potential of supervised argument aspect detection for tracking issue-specific media discourses.

Vocabulary learning is vital to foreign language learning. Correct and adequate feedback is essential to successful and satisfying vocabulary training. However, many vocabulary and language evaluation systems perform on simple rules and do not account for real-life user learning data. This work introduces Multi-Language Vocabulary Evaluation Data Set MuLVE , a data set consisting of vocabulary cards and real-life user answers, labeled indicating whether the user answer is correct or incorrect.

The data source is user learning data from the Phase6 vocabulary trainer. The data set contains vocabulary questions in German and English, Spanish, and French as target language and is available in four different variations regarding pre-processing and deduplication.

The data set is available on the European Language Grid. The detection and extraction of abbreviations from unstructured texts can help to improve the performance of Natural Language Processing tasks, such as machine translation and information retrieval. However, in terms of publicly available datasets, there is not enough data for training deep-neural-networks-based models to the point of generalising well over data.

We performed manual validation over a set of instances and a complete automatic validation for this dataset. We then used it to generate several baseline models for detecting abbreviations and long forms.

The best models achieved an F1-score of 0. The challenges with NLP systems with regards to tasks such as Machine Translation MT , word sense disambiguation WSD and information retrieval make it imperative to have a labelled idioms dataset with classes such as it is in this work. In particular, the following classes are labelled in the dataset: metaphor, simile, euphemism, parallelism, personification, oxymoron, paradox, hyperbole, irony and literal. We obtain an overall inter-annotator agreement IAA score, between two independent annotators, of Many past efforts have been limited in the corpus size and classes of samples but this dataset contains over 20, samples with almost 1, cases of idioms with their meanings from 10 classes or senses.

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We introduce LeSpell, a multi-lingual English, German, Italian, and Czech evaluation data set of spelling mistakes in context that we compiled from seven underlying learner corpora. Our experiments show that existing spellcheckers do not work well with learner data. Thus, we introduce a highly customizable spellchecking component for the DKPro architecture, which improves performance in many settings.

In order to provide suitable text for the target audience, it is necessary to measure its complexity. In this paper we describe subjective experiments to assess the readability of German text.

We compile a new corpus of sentences provided by a German IT service provider. The sentences are annotated with the subjective complexity ratings by two groups of participants, namely experts and non-experts for that text domain. We then extract an extensive set of linguistically motivated features that are supposedly interacting with complexity perception.

We show that a linear regression model with a subset of these features can be a very good predictor of text complexity. In this paper, we address two problems in indexing and querying spoken language corpora with overlapping speaker contributions. First, we look into how token distance and token precedence can be measured when multiple primary data streams are available and when transcriptions happen to be tokenized, but are not synchronized with the sound at the level of individual tokens.

We illustrate the problems, introduce possible solutions and discuss their benefits and drawbacks. This paper introduces DiaBiz, a large, annotated, multimodal corpus of Polish telephone conversations conducted in varied business settings, comprising call centre interactions from nine different domains, i.

The corpus was developed to boost the development of third-party speech recognition engines, dialog systems and conversational intelligence tools for Polish. Its current size amounts to nearly hours of recordings and over 3 million words of transcribed speech.

We present the structure of the corpus, data collection and transcription procedures, challenges of punctuating and truecasing speech transcripts, dialog structure annotation and discuss some of the ecological validity considerations involved in the development of such resources. LaVA corpus contains essays k tokens and k characters excluding whitespaces from foreigners studying at Latvian higher education institutions and who are learning Latvian as a foreign language in the first or second semester, reaching the A1 possibly A2 Latvian language proficiency level.

The corpus has morphological and error annotations. Error analysis and the statistics of the LaVA corpus are also provided in the paper. The filtered parallel corpora range in size from 51 million sentences Spanish-English to k sentences Croatian-English , with the unfiltered raw corpora being up to 2 times larger.

Access to clean, high quality, parallel data in technical domains such as science, engineering, and medicine is needed for training neural machine translation systems for tasks like online dispute resolution and eProcurement.

Our evaluation found that the addition of EuroPat data to a generic baseline improved the performance of machine translation systems on in-domain test data in German, Spanish, French, and Polish; and in translating patent data from Croatian to English.

The corpus has been released under Creative Commons Zero, and is expected to be widely useful for training high-quality machine translation systems, and particularly for those targeting technical documents such as patents and contracts. The exploding amount of user-generated content has spurred NLP research to deal with documents from various digital communication formats tweets, chats, emails, etc.

Using these texts as language resources implies complying with legal data privacy regulations. To protect the personal data of individuals and preclude their identification, we employ pseudonymization. Based on CodE Alltag, a German-language email corpus, we address two tasks.

The first task is to evaluate various architectures for the automatic recognition of privacy-sensitive entities in raw data. The second task examines the applicability of pseudonymized data as training data for such systems since models learned on original data cannot be published for reasons of privacy protection.

Second, we make accessible a tagger for recognizing privacy-sensitive information in German emails and similar text genres, which is trained on already pseudonymized data. The growth of social media has brought with it a massive channel for spreading and reinforcing stereotypes. Although from the perspective of computational linguistics, the detection of this kind of stereotypes is steadily improving, most stereotypes are expressed implicitly and identifying them automatically remains a challenge.

One of the problems we found for tackling this issue is the lack of an operationalised definition of implicit stereotypes that would allow us to annotate consistently new corpora by characterising the different forms in which stereotypes appear.

In this paper, we present thirteen criteria for annotating implicitness which were elaborated to facilitate the subjective task of identifying the presence of stereotypes. We also present NewsCom-Implicitness, a corpus of 1, sentences, of which comprise explicit and implicit racial stereotypes. An experiment was carried out to evaluate the applicability of these criteria.

The results indicate that different criteria obtain different inter-annotator agreement values and that there is a greater agreement when more criteria can be identified in one sentence. Current state of the art acoustic models can easily comprise more than million parameters. This growing complexity demands larger training datasets to maintain a decent generalization of the final decision function.

An ideal dataset is not necessarily large in size, but large with respect to the amount of unique speakers, utilized hardware and varying recording conditions. This enables a machine learning model to explore as much of the domain-specific input space as possible during parameter estimation.

This work introduces Common Phone, a gender-balanced, multilingual corpus recorded from more than It comprises around hours of speech enriched with automatically generated phonetic segmentation. A Wav2Vec 2. The architecture achieved a PER of We conclude that Common Phone provides sufficient variability and reliable phonetic annotation to help bridging the gap between research and application of acoustic models.

This paper presents two-fold contributions: a full revision of the Palestinian morphologically annotated corpus Curras , and a newly annotated Lebanese corpus Baladi. Both corpora can be used as a more general Levantine corpus. Baladi consists of around 9. The inter-annotator evaluation on most features illustrates This revision was also important to ensure that both corpora are compatible and can help to bridge the nuanced linguistic gaps that exist between the two highly mutually intelligible dialects.

Both corpora are publicly available through a web portal. This paper describes a comprehensive annotation study on Japanese judgment documents in civil cases. Our annotation scheme contains annotations of whether tort is accepted by judges as well as its corresponding rationales for explainability purpose. Our annotation scheme extracts decisions and rationales at character-level. The result of the annotation study suggests the proposed annotation scheme can produce a dataset of Japanese LJP at reasonable reliability.

This ignores the complex and subjective nature of HS, which limits the real-life applicability of classifiers trained on these corpora. The annotations are performed by 4 native speakers per language and achieve high 0. Besides describing the corpus creation and presenting insights from a content, error and domain analysis, we explore its data characteristics by training several classification baselines.

In this paper, we describe ParCorFull2. Similar to the previous version, this corpus has been created to address translation of coreference across languages, a phenomenon still challenging for machine translation MT and other multilingual natural language processing NLP applications. The current version of the corpus that we present here contains not only parallel texts for the language pair English-German, but also for English-French and English-Portuguese, which are all major European languages.

The new language pairs belong to the Romance languages. The addition of a new language group creates a need of extension not only in terms of texts added, but also in terms of the annotation guidelines. Both French and Portuguese contain structures not found in English and German. Moreover, Portuguese is a pro-drop language bringing even more systemic differences in the realisation of coreference into our cross-lingual resources.

These differences cause problems for multilingual coreference resolution and machine translation. Our parallel corpus with full annotation of coreference will be a valuable resource with a variety of uses not only for NLP applications, but also for contrastive linguists and researchers in translation studies.

We presentDialogues in Games DinG , a corpus of manual transcriptions of real-life, oral, spontaneous multi-party dialogues between French-speaking players of the board game Catan. Our objective is to make available a quality resource for French, composed of long dialogues, to facilitate their study in the style of Asher et al. In a general dialogue setting, participants share personal information, which makes it impossible to disseminate the resource freely and openly.

In DinG, the attention of the participants is focused on the game, which prevents them from talking about themselves. In addition, we are conducting a study on the nature of the questions in dialogue, through annotation Cruz Blandon et al. This paper describes the experiments carried out during the development of the latest version of Bicleaner, named Bicleaner AI, a tool that aims at detecting noisy sentences in parallel corpora.

The tool, which now implements a new neural classifier, uses state-of-the-art techniques based on pre-trained transformer-based language models fine-tuned on a binary classification task.

After that, parallel corpus filtering is performed, discarding the sentences that have lower probability of being mutual translations. Our experiments, based on the training of neural machine translation NMT with corpora filtered using Bicleaner AI for two different scenarios, show significant improvements in translation quality compared to the previous version of the tool which implemented a classifier based on Extremely Randomized Trees. The corpus was collected while several thousand L2 learners were performing exercises using the Revita language-learning system.

All errors were detected automatically by the system and annotated by type. Part of the corpus was annotated manually—this part was created for further experiments on automatic assessment of grammatical correctness.

The Learner Corpus provides valuable data for studying patterns of grammatical errors, experimenting with grammatical error detection and grammatical error correction, and developing new exercises for language learners.

Automating the collection and annotation makes the process of building the learner corpus much cheaper and faster, in contrast to the traditional approach of building learner corpora. We make the data publicly available. The Universal Morphology UniMorph project is a collaborative effort providing broad-coverage instantiated normalized morphological inflection tables for hundreds of diverse world languages. The project comprises two major thrusts: a language-independent feature schema for rich morphological annotation, and a type-level resource of annotated data in diverse languages realizing that schema.

This paper presents the expansions and improvements on several fronts that were made in the last couple of years since McCarthy et al.

Collaborative efforts by numerous linguists have added 66 new languages, including 24 endangered languages. We have implemented several improvements to the extraction pipeline to tackle some issues, e. We have amended the schema to use a hierarchical structure that is needed for morphological phenomena like multiple-argument agreement and case stacking, while adding some missing morphological features to make the schema more inclusive.

In light of the last UniMorph release, we also augmented the database with morpheme segmentation for 16 languages. Lastly, this new release makes a push towards inclusion of derivational morphology in UniMorph by enriching the data and annotation schema with instances representing derivational processes from MorphyNet. We release an internationalized annotation and human evaluation bundle, called Textinator, along with documentation and video tutorials.

Textinator allows annotating data for a wide variety of NLP tasks, and its user interface is offered in multiple languages, lowering the entry threshold for domain experts. The latter is, in fact, quite a rare feature among the annotation tools, that allows controlling for possible unintended biases introduced due to hiring only English-speaking annotators.

We illustrate the rarity of this feature by presenting a thorough systematic comparison of Textinator to previously published annotation tools along 9 different axes with internationalization being one of them. To encourage researchers to design their human evaluation before starting to annotate data, Textinator offers an easy-to-use tool for human evaluations allowing importing surveys with potentially hundreds of evaluation items in one click.

We finish by presenting several use cases of annotation and evaluation projects conducted using pre-release versions of Textinator. Over the past decades, the number of episodes of cyber aggression occurring online has grown substantially, especially among teens. Most solutions investigated by the NLP community to curb such online abusive behaviors consist of supervised approaches relying on annotated data extracted from social media.

However, recent studies have highlighted that private instant messaging platforms are major mediums of cyber aggression among teens.

As such interactions remain invisible due to the app privacy policies, very few datasets collecting aggressive conversations are available for the computational analysis of language.

In order to overcome this limitation, in this paper we present the CyberAgressionAdo-V1 dataset, containing aggressive multiparty chats in French collected through a role-playing game in high-schools, and annotated at different layers. We describe the data collection and annotation phases, carried out in the context of a EU and a national research projects, and provide insightful analysis on the different types of aggression and verbal abuse depending on the targeted victims individuals or communities emerging from the collected data.

There has been a lot of research in identifying hate posts from social media because of their detrimental effects on both individuals and society. However, there is a lack of hate speech datasets compared to English, and a multilingual pre-trained model often contains fewer tokens for other languages. This paper attempts to contribute to hate speech identification in Finnish by constructing a new hate speech dataset that is collected from a popular forum Suomi Automatic post-editing APE refers to a research field that aims to automatically correct errors included in the translation sentences derived by the machine translation system.

This study has several limitations, considering the data acquisition, because there is no official dataset for most language pairs. Moreover, the amount of data is restricted even for language pairs in which official data has been released, such as WMT.

To solve this problem and promote universal APE research regardless of APE data existence, this study proposes a method for automatically generating APE data based on a noising scheme from a parallel corpus.

Particularly, we propose a human mimicking errors-based noising scheme that considers a practical correction process at the human level. We propose a precise inspection to attain high performance, and we derived the optimal noising schemes that show substantial effectiveness. Through these, we also demonstrate that depending on the type of noise, the noising scheme-based APE data generation may lead to inferior performance.

In addition, we propose a dynamic noise injection strategy that enables the acquisition of a robust error correction capability and demonstrated its effectiveness by comparative analysis.

This study enables obtaining a high performance APE model without human-generated data and can promote universal APE research for all language pairs targeting English. Cross-lingual transfer learning without labeled target language data or parallel text has been surprisingly effective in zero-shot cross-lingual classification, question answering, unsupervised machine translation, etc.

However, some recent publications have claimed that domain mismatch prevents cross-lingual transfer, and their results show that unsupervised bilingual lexicon induction UBLI and unsupervised neural machine translation UNMT do not work well when the underlying monolingual corpora come from different domains e.

In this work, we show how a simple initialization regimen can overcome much of the effect of domain mismatch in cross-lingual transfer. In all cases, our results challenge the conclusions of prior work by showing that proper initialization can recover a large portion of the losses incurred by domain mismatch. Clinical phenotyping enables the automatic extraction of clinical conditions from patient records, which can be beneficial to doctors and clinics worldwide.

However, current state-of-the-art models are mostly applicable to clinical notes written in English. We therefore investigate cross-lingual knowledge transfer strategies to execute this task for clinics that do not use the English language and have a small amount of in-domain data available.

Our results reveal two strategies that outperform the state-of-the-art: Translation-based methods in combination with domain-specific encoders and cross-lingual encoders plus adapters. We find that these strategies perform especially well for classifying rare phenotypes and we advise on which method to prefer in which situation.

Our results show that using multilingual data overall improves clinical phenotyping models and can compensate for data sparseness. Translation of the noisy, informal language found in social media has been an understudied problem, with a principal factor being the limited availability of translation corpora in many languages. To address this need we have developed a new corpus containing over , translations of microblog posts that supports translation of thirteen languages into English.

We are releasing these data as the Multilingual Microblog Translation Corpus to support futher research in translation of informal language. We establish baselines using this new resource, and we further demonstrate the utility of the corpus by conducting experiments with fine-tuning to improve translation quality from a high performing neural machine translation NMT system.

Humans constantly deal with multimodal information, that is, data from different modalities, such as texts and images. In order for machines to process information similarly to humans, they must be able to process multimodal data and understand the joint relationship between these modalities. We use the multimodal and multilingual corpus How2 Sanabria et al. Our experiments on the Portuguese-English multimodal translation task using the How2 dataset demonstrate the efficacy of cross-lingual visual pretraining.

We achieved a BLEU score of Recently, we have seen an increasing interest in the area of speech-to-text translation. This has led to astonishing improvements in this area. In contrast, the activities in the area of speech-to-speech translation is still limited, although it is essential to overcome the language barrier.

We believe that one of the limiting factors is the availability of appropriate training data. We address this issue by creating LibriS2S, to our knowledge the first publicly available speech-to-speech training corpus between German and English.

For this corpus, we used independently created audio for German and English leading to an unbiased pronunciation of the text in both languages. This allows the creation of a new text-to-speech and speech-to-speech translation model that directly learns to generate the speech signal based on the pronunciation of the source language.

Using this created corpus, we propose Text-to-Speech models based on the example of the recently proposed FastSpeech 2 model that integrates source language information.

We do this by adapting the model to take information such as the pitch, energy or transcript from the source speech as additional input. This paper presents a fine-grained test suite for the language pair German—English. The test suite is based on a number of linguistically motivated categories and phenomena and the semi-automatic evaluation is carried out with regular expressions.

We describe the creation and implementation of the test suite in detail, providing a full list of all categories and phenomena. Furthermore, we present various exemplary applications of our test suite that have been implemented in the past years, like contributions to the Conference of Machine Translation, the usage of the test suite and MT outputs for quality estimation, and the expansion of the test suite to the language pair Portuguese—English.

We describe how we tracked the development of the performance of various systems MT systems over the years with the help of the test suite and which categories and phenomena are prone to resulting in MT errors. For the first time, we also make a large part of our test suite publicly available to the research community. Recent studies in cross-lingual learning using multilingual models have cast doubt on the previous hypothesis that shared vocabulary and joint pre-training are the keys to cross-lingual generalization.

We introduce a method for transferring monolingual models to other languages through continuous pre-training and study the effects of such transfer from four different languages to English. Our experimental results on GLUE show that the transferred models outperform an English model trained from scratch, independently of the source language.

After probing the model representations, we find that model knowledge from the source language enhances the learning of syntactic and semantic knowledge in English. We present a dataset containing source code solutions to algorithmic programming exercises solved by hundreds of Bachelor-level students at the University of Hamburg.

The dataset contains a set of solutions to a total of 21 tasks written in Java as well as Python and a total of over individual solutions. All solutions were submitted through Moodle and the Coderunner plugin and passed a number of test cases including randomized tests , such that they can be considered as working correctly. All students whose solutions are included in the dataset gave their consent into publishing their solutions. The solutions are pseudonymized with a random solution ID.

Included in this paper is a short analysis of the dataset containing statistical data and highlighting a few anomalies e. We plan to extend the dataset with tasks and solutions from upcoming courses. In this work, we conduct a quantitative linguistic analysis of the language usage patterns of multilingual peer supporters in two health-focused WhatsApp groups in Kenya comprising of youth living with HIV.

Even though the language of communication for the group was predominantly English, we observe frequent use of Kiswahili, Sheng and code-mixing among the three languages. We present an analysis of language choice and its accommodation, different functions of code-mixing, and relationship between sentiment and code-mixing. To explore the effectiveness of off-the-shelf Language Technologies LT in such situations, we attempt to build a sentiment analyzer for this dataset.

Our experiments demonstrate the challenges of developing LT and therefore effective interventions for such forums and languages. We provide recommendations for language resources that should be built to address these challenges. Frame shift is a cross-linguistic phenomenon in translation which results in corresponding pairs of linguistic material evoking different frames.

The ability to predict frame shifts would enable semi- automatic creation of multilingual frame annotations and thus speeding up FrameNet creation through annotation projection. Here, we first characterize how frame shifts result from other linguistic divergences such as translational divergences and construal differences.

Then, we propose the Frame Shift Prediction task and demonstrate that our graph attention networks, combined with auxiliary training, can learn cross-linguistic frame-to-frame correspondence and predict frame shifts. Cued Speech is a communication system developed for deaf people to complement speechreading at the phonetic level with hands. This visual communication mode uses handshapes in different placements near the face in combination with the mouth movements of speech to make the phonemes of spoken language look different from each other.

It consists in about 4 hours of audio and HD video recordings of 23 participants. It can be used for any further research or teaching purpose. The corpus includes orthographic transliteration and other phonetic annotations on 5 of the recorded topics, i. It contains hours of read speech from Icelandic children aged between 4 to 17 years. The test portion was meticulously selected to cover a wide range of ages as possible; we aimed to have exactly the same amount of data per age range.

Additionally, we present baseline experiments and results using Kaldi. It is the first, publicly available dataset containing unscripted, Norwegian speech designed for training of automatic speech recognition ASR systems. The recordings are manually transcribed and annotated with language codes and speakers, and there are detailed metadata about the speakers. The transcriptions exist in both normalized and non-normalized form, and non-standardized words are explicitly marked and annotated with standardized equivalents.

To test the usefulness of this dataset, we have compared an ASR system trained on the NPSC with a baseline system trained on only manuscript-read speech.

These systems were tested on an independent dataset containing spontaneous, dialectal speech. The NPSC-trained system performed significantly better, with a During these meetings both Frisian and Dutch are spoken, and code switching between both languages shows up frequently.

Adapting a speech recognizer for the council meeting domain is challenging because of acoustic background noise, speaker overlap and the jargon typically used in council meetings. To train the new recognizer, we used the radio broadcast materials utilized for the development of the FAME!

The council meeting recordings consist of 49 hours of speech, with 26 hours of Frisian speech and 23 hours of Dutch speech. Furthermore, from the same sources, we obtained texts in the domain of council meetings containing 11 million words; 1. We describe the methods used to train the new recognizer, report the observed word error rates, and perform an error analysis on remaining errors. There is a need for a simple method of detecting early signs of dementia which is not burdensome to patients, since early diagnosis and treatment can often slow the advance of the disease.

Several studies have explored using only the acoustic and linguistic information of conversational speech as diagnostic material, with some success.

Using our elderly speech corpus and dementia test results, we propose an SVM-based screening method which can detect dementia using the acoustic features of conversational speech even when regional dialects are present.

We accomplish this by omitting some acoustic features, to limit the negative effect of differences between dialects. When speech from four regions was used in a second experiment, the discrimination rate fell to This is an on-going research project, and additional investigation is needed to understand differences in the acoustic characteristics of phoneme units in the conversational speech collected from these four regions, to determine whether the removal of formants and other features can improve the dementia detection rate.

Spoken medical dialogue systems are increasingly attracting interest to enhance access to healthcare services and improve quality and traceability of patient care. In this paper, we focus on medical drug prescriptions acquired on smartphones through spoken dialogue. However, there is a lack of speech corpora to develop such systems since most of the related corpora are in text form and in English. To facilitate the research and development of spoken medical dialogue systems, we present, to the best of our knowledge, the first spoken medical drug prescriptions corpus, named PxNLU.

It contains 4 hours of transcribed and annotated dialogues of drug prescriptions in French acquired through an experiment with 55 participants experts and non-experts in prescriptions. We also present some experiments that demonstrate the interest of this corpus for the evaluation and development of medical dialogue systems.

Commercial alternatives e. These reasons motivate that just a small amount of medical staff employs speech technology in the Netherlands. On the semantic level it specifically targets automatic transcription of doctor-patient consultation recordings with a focus on the use of medicines.

Despite the acoustic challenges and linguistic complexity of the domain, we reduced the word error rate WER by 5. The proposed method could be employed for ASR domain adaptation to other domains with sensitive and special category data.

These promising results allow us to apply this methodology on highly sensitive audiovisual recordings of patient consultations at the Netherlands Institute for Health Services Research Nivel. Machine learning methodologies can be adopted in cultural applications and propose new ways to distribute or even present the cultural content to the public.

For instance, speech analytics can be adopted to automatically generate subtitles in theatrical plays, in order to among other purposes help people with hearing loss. Apart from a typical speech-to-text transcription with Automatic Speech Recognition ASR , Speech Emotion Recognition SER can be used to automatically predict the underlying emotional content of speech dialogues in theatrical plays, and thus to provide a deeper understanding how the actors utter their lines.

However, real-world datasets from theatrical plays are not available in the literature. In this work we present GreThE, the Greek Theatrical Emotion dataset, a new publicly available data collection for speech emotion recognition in Greek theatrical plays. The dataset contains utterances from various actors and plays, along with respective valence and arousal annotations.

Towards this end, multiple annotators have been asked to provide their input for each speech recording and inter-annotator agreement is taken into account in the final ground truth generation. In addition, we discuss the results of some indicative experiments that have been conducted with machine and deep learning frameworks, using the dataset, along with some widely used databases in the field of speech emotion recognition.

Synthetic voices are increasingly used in applications that require a conversational speaking style, raising the question as to which type of training data yields the most suitable speaking style for such applications. This study compares voices trained on three corpora of equal size recorded by the same speaker: an audiobook character speech dialogue corpus, an audiobook narrator speech corpus, and a neutral-style sentence-based corpus.

The voices were trained with three text-to-speech synthesisers: two hidden Markov model-based synthesisers and a neural synthesiser. An evaluation study tested the suitability of their speaking style for use in customer service voice chatbots.

Independently of the synthesiser used, the voices trained on the character speech corpus received the lowest, and those trained on the neutral-style corpus the highest scores. However, the evaluation results may have been confounded by the greater acoustic variability, less balanced sentence length distribution, and poorer phonemic coverage of the character speech corpus, especially compared to the neutral-style corpus.

View editor publications. Buying options eBook EUR Price includes VAT Finland. Softcover Book EUR Learn about institutional subscriptions. Table of contents 36 papers Search within book Search. Page 1 Navigate to page number of 3. Front Matter Pages i-xxi. Dunn Pages Back to top. Keywords artificial intelligence computer network computer science computer systems computer vision data mining databases deep learning hci health informatics human-computer interaction image processing information retrieval machine learning neural networks pattern recognition signal processing.

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