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Paid full-time PhD Position on Perspectivism and Disagreement in Natural Language Processing
by d.braun＠utwente.nl 10 Nov '23

10 Nov '23

Job posting: 4-year paid full-time PhD Position on Perspectivism and Disagreement in Natural Language Processing Employer: University of Twente Location: Enschede, Netherlands Deadline: 4th December 2023 Application website and more details: https://utwentecareers.nl/en/vacancies/1505/phd-position-on-perspectivism-a… Do you want to work on Natural Language Processing and aspects of fairness and representation in Machine Learning? We are looking for a highly motivated and curious PhD candidate to join the High-tech Business & Entrepreneurship Department (HBE) at the University of Twente in the Netherlands. Your profile - A background in Natural Language Processing; - A strong interest in ethical and societal aspects of AI; - You hold, or will shortly acquire, a master’s degree in Computer Science, Computational Linguistics, Information Systems, or a related discipline; - Demonstrated programming skills in Python, JavaScript or similar languages; - First experience with user studies is a plus; - Capable of conducting independent research and willing to develop writing and publication skills; - Proficient in written and oral English (C1; above IELTS 7 or equivalent); - A positive team spirit and enthusiasm for working in an interdisciplinary and internationally oriented environment. Our offer We encourage high responsibility and independence, while collaborating with colleagues, researchers, other university staff and partners. We follow the terms of employment by the Dutch Collective Labour Agreement for Universities (CAO). Our offer contains: a full-time 4-year PhD position with a qualifier in the first year; excellent mentorship in a stimulating research environment with excellent facilities; and a personal development program within the Twente Graduate School. It also includes: - Gross monthly salary of € 2.770 in the first year, increasing each year up to € 3.539 in the fourth year; - Excellent benefits including a holiday allowance of 8% of the gross annual salary, an end-of-year bonus of 8.3%, and a solid pension scheme; - 29 holidays per year in case of full-time employment; - A training programme as part of the Twente Graduate School where you and your supervisors will determine a plan for a suitable education and supervision; - A green campus with free access to sports facilities and an international scientific community; - A family-friendly institution that offers parental leave (both paid and unpaid); - A full status as an employee at the UT, including pension, health care benefits and good secondary conditions are part of our collective labour agreement CAO-NU for Dutch universities. How to apply Are you interested in being part of our team? Please submit your application before the 4th of December and include: A cover letter (maximum 2 pages A4), emphasizing your specific interest, qualifications, and motivations to apply for this position; A Curriculum Vitae, including a list of all courses attended and grades obtained, and, if applicable, a list of publications. Additional information can be acquired via email from d.braun(a)utwente.nl. Applications can be submitted via https://utwentecareers.nl/en/vacancies/1505/phd-position-on-perspectivism-a…

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Call for participation: Shared Task on Word Embedding Evaluation for Ancient and Historical Languages (SIGTYP @ EACL)
by Oksana Dereza 10 Nov '23

10 Nov '23

Dear colleagues, [Apologies for cross-posting] In 2024, SIGTYP is hosting a *Shared Task on Word Embedding Evaluation for Ancient and Historical Languages*: https://sigtyp.github.io/st2024.html The workshop will be co-located with EACL. *Summary* In recent years, sets of downstream tasks called benchmarks have become a very popular, if not default, method to evaluate general-purpose word and sentence embeddings. Starting with decaNLP (McCann et al., 2018) and SentEval (Conneau & Kiela, 2018), multitask benchmarks for NLU keep appearing and improving every year. However, even the largest multilingual benchmarks, such as XGLUE, XTREME, XTREME-R or XTREME-UP (Hu et al., 2020; Liang et al., 2020; Ruder et al., 2021, 2023), only include modern languages. When it comes to ancient and historical languages, scholars mostly adapt/translate intrinsic evaluation datasets from modern languages or create their own diagnostic tests. We argue that there is a need for a universal evaluation benchmark for embeddings learned from ancient and historical language data and view this shared task as a proving ground for it. The shared task involves solving the following problems for 12+ ancient and historical languages that belong to 4 language families and use 6 different scripts. Participants will be invited to describe their system in a paper for the SIGTYP workshop proceedings. The task organisers will write an overview paper that describes the task and summarises the different approaches taken, and analyses their results. *Subtasks* For subtask A, participants are not allowed to use any additional data; however, they can reduce and balance provided training datasets if they see fit. For subtask B, participants are allowed to use any additional data in any language, including pre-trained embeddings and LLMs. A. Constrained 1. POS-tagging 2. Full morphological annotation 3. Lemmatisation B. Unconstrained 1. POS-tagging 2. Full morphological annotation 3. Lemmatisation 4. Filling the gaps - Word-level - Character-level *Data* For tasks 1-3, we use Universal Dependencies v. 2.12 data (Zeman et al., 2023) in 11 ancient and historical languages, complemented by 5 Old Hungarian codices from the MGTSZ website (HAS Research Institute for Linguistics, 2018) that are annotated to the same standard as the corpora available through UD. For task 4, we add historical Irish data from CELT (Ó Corráin et al., 1997), Corpas Stairiúil na Gaeilge (Acadamh Ríoga na hÉireann, 2017), and digital editions of the St. Gall glosses (Bauer et al., 2017) and the Würzburg glosses (Doyle, 2018) as a case study of how performance may vary on different historical stages of the same language. We set the upper temporal boundary to 1700 CE and do not include texts created later than this date in our dataset. List of languages: - Ancient Greek - Ancient Hebrew - Classical Chinese - Coptic - Gothic - Classical, Late & Medieval Latin - Medieval Icelandic - Old Church Slavonic - Old East Slavic - Old French - Old Hungarian - Old, Middle & Early Modern Irish - Vedic Sanskrit *Important dates* *05 Nov 2023*: Release of training and validation data *02 Jan 2024*: Release of test data *08 Jan 2024*: Submission of the systems *13 Jan 2024*: Notification of results *20 Jan 2024*: Submission of shared task papers *27 Jan 2024*: Notification of acceptance to authors *03 Feb 2024*: Camera-ready *15 Mar 2024*: Video recordings due *21/22 Mar 2024*: SIGTYP workshop *Important links* - *Registration form* <https://docs.google.com/forms/d/e/1FAIpQLSdINgMfzzZGIZ-uBVQhvyndB6yeaaj-wT7…> - Data + detailed description: https://github.com/sigtyp/ST2024 *Task organisers* - Oksana Dereza, Insight SFI Research Centre for Data Analytics, Data Science Institute, University of Galway - Priya Rani, SFI Centre for Research and Training in AI, Data Science Institute, University of Galway - Atul Kr. Ojha, Insight SFI Research Centre for Data Analytics, Data Science Institute, University of Galway - Adrian Doyle, Insight SFI Research Centre for Data Analytics, Data Science Institute, University of Galway - Pádraic Moran, School of Languages, Literatures and Cultures, Moore Institute, University of Galway - John P. McCrae, Insight SFI Research Centre for Data Analytics, Data Science Institute, University of Galway *Contact details* - Oksana: oksana.dereza(a)insight-centre.org - Priya: priya.rani(a)insight-centre.org Best wishes, Oksana and the organisers -- [image: https://nuig.insight-centre.org/] <https://www.insight-centre.org/> Oksana Dereza | PhD student on the Cardamom <http://cardamom.insight-centre.org/> project | Unit for Linguistic Data | Insight Centre for Data Analytics | Data Science Institute | University of Galway Oksana Dereza | Iarrthóir PhD ar thionscadal Cardamom <http://cardamom.insight-centre.org/> | An tAonad um Shonraí Teangeolaíocha | Insight, Ionad na hAnailísíochta Sonraí | Institiúid Eolaíochta Sonraí | Ollscoil na Gaillimhe

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PhD Studentship in e-Health and/or Sign Language Translation] @ Queen's University Belfast, UK
by Mohammed Hasanuzzaman 10 Nov '23

10 Nov '23

Dear Colleagues, I am looking for a PhD student in e-Health and/or Sign Language Translation at the School of School of Electronics, Electrical Engineering and Computer Science, Queen's University in Belfast, UK. The details of the project can be found below: 1. https://www.qub.ac.uk/courses/postgraduate-research/phd-opportunities/causa… 2. https://www.qub.ac.uk/courses/postgraduate-research/phd-opportunities/endto… Requirements: The successful candidate is expected to have a solid background in Machine Learning, Statistics, computer science or related discipline. The minimum academic requirement for admission to a research degree programme is normally an Upper Second Class Honours degree from a UK or ROI HE provider, or an equivalent qualification acceptable to the University. Refer to the official application process for details. Application Process: For enquiries, please send an email to Dr. Mohammed Hasanuzzaman at m.hasanuzzaman(a)qub.ac.uk with your CV and transcript as well as a brief description of your research interests. Unfortunately, due to a high volume of inquiries, I may not be able to respond to all emails. Submit your formal application through the following link. https://dap.qub.ac.uk/portal/user/u_login.php Your application should be clearly marked as EEECS/2024/MH1 and/or EEECS/2024/MH2 to ensure consideration for funding About Queen's: The Queen's University Belfast, founded almost two centuries ago, is one of the oldest universities in the United Kingdom. As a member of the prestigious Russell Group, Queen’s is one of the UK’s 24 leading research-intensive universities (ranked 13th in the UK for research intensity). Queen's has 15 subjects in the top 200 in the world (QS World University Rankings 2023). Five of those subjects are in the World Top 100 (QS World Rankings by subject 2023). For more details: https://www.qub.ac.uk/Study/Why-Study-at-Queens/rankings-and-reputation/ *International students are welcome to apply, but additional funding ( https://www.qub.ac.uk/Study/international-students/international-scholarshi…) or personal finances will be required to cover the difference between home (UK) and overseas fees. Best regards, Mohammed ------------------------------------------------------------------------------------------------------ *Dr. Mohammed HasanuzzamanLecturer/Assistant Professor**Queen's University Belfast <https://www.qub.ac.uk/>, UK * *&Munster Technological University <https://www.mtu.ie/>, Ireland* *Funded Investigator, ADAPT Centre- <https://www.adaptcentre.ie/> A <https://www.adaptcentre.ie/>* World-Leading SFI Research Centre <https://www.adaptcentre.ie/> *C**hercheur Associé*, GREYC UMR CNRS 6072 Research Centre, France <https://www.greyc.fr/en/home/> *Associate Editor:* * IEEE Transactions on Affective Computing, Nature Scientific Reports, IEEE Transactions on Computational Social Systems, ACM TALLIP, PLOS One, Computer Speech and Language**Website: **https://mohammedhasanuzzaman.github.io/ <https://mohammedhasanuzzaman.github.io/>* [image: Mailtrack] <https://mailtrack.io?utm_source=gmail&utm_medium=signature&utm_campaign=sig…> Sender notified by Mailtrack <https://mailtrack.io?utm_source=gmail&utm_medium=signature&utm_campaign=sig…> 10/11/23, 14:20:50

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3-year PhD position in Automatic Argumentation Mining in French Legal Decisions (Inria Lille, University of Lille, and LexisNexis France)
by Pascal Denis 10 Nov '23

10 Nov '23

Hi there, Could you please distribute the following job offer? Thanks. Best, Pascal ------------------------------------------------------------------------------------- We invite applications for a 3-year PhD position co-funded by Inria, the French national research institute in Computer Science and Applied Mathematics, and LexisNexis France, leader of legal information in France and subsidiary of the RELX Group. The overall objective of this project is to develop an automated system for detecting argumentation structures in French legal decisions, using recent machine learning-based approaches (i.e. deep learning approaches). In the general case, these structures take the form of a directed labeled graph, whose nodes are the elements of the text (propositions or groups of propositions, not necessarily contiguous) which serve as components of the argument, and edges are relations that signal the argumentative connection between them (e.g., support, offensive). By revealing the argumentation structure behind legal decisions, such a system will provide a crucial milestone towards their detailed understanding, their use by legal professionals, and above all contributes to greater transparency of justice. The main challenges and milestones of this project start with the creation and release of a large-scale dataset of French legal decisions annotated with argumentation structures. To minimize the manual annotation effort, we will resort to semi-supervised and transfer learning techniques to leverage existing argument mining corpora, such as the European Court of Human Rights (ECHR) corpus, as well as annotations already started by LexisNexis. Another promising research direction, which is likely to improve over state-of-the-art approaches, is to better model the dependencies between the different sub-tasks (argument span detection, argument typing, etc.) instead of learning these tasks independently. A third research avenue is to find innovative ways to inject the domain knowledge (in particular the rich legal ontology developed by LexisNexis) to enrich enrich the representations used in these models. Finally, we would like to take advantage of other discourse structures, such as coreference and rhetorical relations, conceived as auxiliary tasks in a multi-tasking architecture. The successful candidate holds a Master's degree in computational linguistics, natural language processing, machine learning, ideally with prior experience in legal document processing and discourse processing. Furthermore, the candidate will provide strong programming skills, expertise in machine learning approaches and is eager to work at the interplay between academia and industry. The position is affiliated with the MAGNET [1], a research group at Inria, Lille, which has expertise in Machine Learning and Natural Language Processing, in particular Discourse Processing. The PhD student will also work in close collaboration with the R&D team at LexisNexis France, who will provide their expertise in the legal domain and the data they have collected. Applications will be considered until the position is filled. However, you are encouraged to apply early as we shall start processing the applications as and when they are received. Applications, written in English or French, should include a brief cover letter with research interests and vision, a CV (including your contact address, work experience, publications), and contact information for at least 2 referees. Applications (and questions) should be sent to Pascal Denis (pascal.denis(a)inria.fr). The starting date of the position is 1 November 2022 or soon thereafter, for a total of 3 full years. Best regards, Pascal Denis [1] https://team.inria.fr/magnet/ [2] https://www.lexisnexis.fr/ -- Pascal ---- Pour une évaluation indépendante, transparente et rigoureuse ! Je soutiens la Commission d'Évaluation de l'Inria. ---- +++++++++++++++++++++++++++++++++++++++++++++++ Pascal Denis Equipe MAGNET, INRIA Lille Nord Europe Bâtiment B, Avenue Heloïse Parc scientifique de la Haute Borne 59650 Villeneuve d'Ascq Tel: ++33 3 59 35 87 24 Url: http://researchers.lille.inria.fr/~pdenis/ +++++++++++++++++++++++++++++++++++++++++++++++

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Postdoctoral position in computational linguistics with specialisation in language grounding to vision, robotics, and beyond (University of Gothenburg, Sweden)
by Simon Dobnik 09 Nov '23

09 Nov '23

[apologies for x-posting] Postdoctoral position in computational linguistics with specialisation in language grounding to vision, robotics, and beyond University of Gothenburg, Sweden Project Description: The broad focus of this position is computational modelling of language (computational linguistics, natural language processing or language technology) in the context of data from other modalities such as vision, perception and action from the perspective of human-centred AI. The topics relevant for this position are computational modelling of language and perception, human-robot interaction, situated spoken dialogue systems, and computational representation of meaning (semantics). The work will be done within the Cognitive Systems group (lead by Simon Dobnik, https://www.gu.se/en/about/find-staff/simondobnik ) which one of the four research groups within The Centre for Linguistic Theory and Studies in Probability (CLASP), https://www.gu.se/clasp and is devoted to research and advanced training in the application of probabilistic modelling and machine learning methods to core issues in linguistic theory and cognition. The postdoctoral candidate will will have an opportunity further their scientific skills and advance the scientific filed by conducting research in collaboration with the research group by connecting ideas from several of the following areas: - Computational semantics, - Grounding language in action and perception, - Generation and understanding of spatial language, - Generation of image descriptions, visual question answering, visual dialogue - Referring in situated dialogue, - Situated agents / robots and instruction generation and following, - Machine learning with neural networks, - Cross-domain model transfer, - Learning from small data, - Combining top-down (expert-driven) and bottom-up (dataset-driven) models, - Reasoning and inference including Bayesian inference, - Model interpretation, testing and evaluation of unwanted social bias, - Crowd-sourcing for collection and evaluation of research data. Application Deadline: November 21, 2023 (end of the day, GMT+1). Formal announcement and application procedure: https://web103.reachmee.com/ext/I005/1035/job?site=7&lang=UK&validator=9b89… (English) and https://web103.reachmee.com/ext/I005/1035/job?site=6&lang=SE&validator=3038… (Swedish) Contact: For more information about the research / project focus relevant to the position, send an email to Simon Dobnik, Professor of Computational Linguistics, simon.dobnik(a)gu.se <mailto:simon.dobnik@gu.se> For other questions, please contact Sharid Loáiciga, Associate Senior Lecturer, +46(0) 31-786 59 42, sharid.loaiciga(a)gu.se <mailto:sharid.loaiciga@gu.se> — Simon Dobnik Professor of Computational Linguistics CLASP & FLoV, University of Gothenburg https://www.gu.se/en/about/find-staff/simondobnik

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[CfP] First Call for Papers: The third UnImplicit workshop at EACL 2024
by Sandro Pezzelle 09 Nov '23

09 Nov '23

* Apologies for cross-posting * We are happy to announce the third UnImplicit workshop, which will be co-located with EACL 2024. Workshop: March 21 or 22, 2024 (TBD on which of the two days) EACL Conference: March 17-22, 2024 Website: https://unimplicit2024.github.io/ Paper submission: https://openreview.net/group?id=eacl.org/EACL/2024/Workshop/UnImplicit * Paper submission deadline: December 18, 2023 * * Paper submission deadline for papers with ARR Reviews: January 17, 2024 * Real language is underspecified, vague, and ambiguous. Indeed, past work (Zipf, 1949; Piantadosi, 2012) has suggested that ambiguity may be an inextricable feature of natural language, resulting from competing communicative pressures. Resolving the meaning of language is a never-ending process of making inferences based on implicit knowledge. For example, we know that ``the girl saw the man with the telescope'' is ambiguous and could refer to two situations, while ``the girl saw the man with the hamburger'' is not, or that ``near'' in ``the house near the airport'' and ``the ant near the crumb'' does not refer to the same distance. Being able to capture this kind of knowledge is central to building systems with a human-like understanding of language, as well as providing a full account of natural language itself. We welcome submissions related to, but not limited to, the following topics: * Creating corpora or new annotations for underspecified, vague, or ambiguous language * Studies of annotator disagreement * Methods of resolving underspecification, vagueness, or ambiguity * Studies of how multimodal settings interact with underspecification in language * Ambiguities in non-linguistic domains, like images or videos * Perspectives on the role of vagueness and ambiguity in NLP Similar to the first two editions, we would accept theoretical and practical contributions (long, short, and non-archival) on all aspects related to the workshop topic. If you are interested, you can check out the last two UnImplicit workshops held at ACL 2021<https://unimplicit.github.io/#> and NAACL 2022<https://unimplicit2022.github.io>. Important Dates ============= Dec. 18, 2023: Workshop paper deadline (OpenReview) Jan. 17, 2024: Deadline to commit papers with ARR Reviews (OpenReview) Jan. 20, 2024: Notification of Acceptance Jan. 30, 2024: Camera-ready papers due Mar. 21-22, 2024: Workshop Dates (TBD on which of the two days) All deadlines are 11.59 pm UTC -12h (“anywhere on Earth”). Submissions ========== We invite two types of submissions: 1. Archival: long (up to 8 pages) or short (up to 4 pages) papers, with unlimited references. These papers should report on complete, original, and unpublished research and cannot be 'under submission' elsewhere. If accepted, archival papers will appear in the workshop proceedings. 2. Non-archival: Extended abstracts (up to 2 pages) or copy of submission/publication, which can take two forms: Works in progress, that are not yet mature enough for a full submission. Or already published work, or work currently under submission elsewhere, which can be submitted in the form of the original abstract and a copy of the submission/publication. We are not enforcing any anonymity period. The workshop will run its review process, and papers can be submitted directly to OpenReview (https://openreview.net/group?id=eacl.org/EACL/2024/Workshop/UnImplicit) on Dec. 18th, 2023. It is also possible to submit a paper accompanied by reviews from the ACL Rolling Review system, or a paper that has been rejected from EACL, or a Findings paper looking for a presentation slot, by Jan. 17th, 2024 (please use the following form for this type of submission: ). Both papers and extended abstracts must follow the EACL 2024 format. Accepted papers and extended abstracts must be presented at the workshop and at least one author must be registered for the workshop. Workshop organizers ========== Valentina Pyatkin, AI2 and University of Washington Elias Stengel-Eskin, UNC Chapel Hill Alisa Liu, University of Washington Sandro Pezzelle, University of Amsterdam Daniel Fried, Carnegie Mellon University Sandro -- on behalf of the organizing team --- Sandro Pezzelle ILLC - Institute for Logic, Language & Computation University of Amsterdam sandropezzelle.github.io<http://sandropezzelle.github.io/>

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[CfP] Third Call for Papers: Applied Sciences (IF: 2.7) Special Issue on "Application of Artificial Intelligence Methods in Processing of Emotions, Decisions and Opinions"
by Ptaszynski Michal 09 Nov '23

09 Nov '23

Dear Colleagues Apologies for cross-posting. This is Michal Ptaszynski from Kitami Institute of Technology, Japan. We are accepting papers for the Applied Sciences journal (Impact Factor: 2.7) special issue on "Application of Artificial Intelligence Methods in Processing of Emotions, Decisions and Opinions". The new deadline for manuscript submission is March 31, 2024, but your paper will be sent for review as soon as it is submitted and will be published shortly after being accepted. We hope you will consider submitting your paper. https://www.mdpi.com/journal/applsci/special_issues/NTMDOE41MY Best regards, Michal PTASZYNSKI, Ph.D., Associate Professor Department of Computer Science Kitami Institute of Technology, 165 Koen-cho, Kitami, 090-8507, Japan TEL/FAX: +81-157-26-9327 michal(a)mail.kitami-it.ac.jp ============================================ Applied Sciences (Impact Factor: 2.7) Special Issue on "Application of Artificial Intelligence Methods in Processing of Emotions, Decisions and Opinions" Special Issue Information During recent years, social infrastructure has become irreversibly linked to the Internet through its everyday manifestations, such as social networking services (Twitter, Facebook, etc.). Every second this new tangible information-based reality provides large amounts of data filled with 1) emotional expressions; 2) people's opinions on various topics; and 3) their reasoning, revealing their decision-making processes. As these three categories are also closely interrelated with each other, they should be studied together to obtain a more robust view on all of the topics involved. This, as never before, provides an opportunity for the development and application of natural language processing methods, in particular those regarding such topics as emotion processing, decision-making, and opinion mining. For this issue, we invite high-quality papers from researchers with an interest in knowing more about those topics and their connection to the world we live in through opinion and sentiment analysis, recommendation systems, web mining, automated decision-making, etc. We also invite papers on the topic of using Natural Language Processing tools and methods to process emotions, metaphors, ethics, or other phenomena related to human activities. List of Topics The Special Issue will invite papers on topics listed, but not limited to the following: - opinion mining - decision support systems - emotion detection - sentiment analysis - natural language processing - computational linguistics - NLP applications - natural language generation - emotional language processing - humor and joke processing - deceptive language detection - emoticon processing - automatic cyberbullying detection - fake news detection - abusive language processing - story generation - poetry generation - cognitive agents Guest Editors Dr. Pawel Dybala Dr. Rafal Rzepka Dr. Michal Ptaszynski Michal Ptaszynski michal.ptaszynski(a)gmail.com

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Call for Shared Task Participation - CASE @ EACL 2024: Climate Activism Stance and Hate Event Detection & previous shared tasks
by ali hürriyetoglu 09 Nov '23

09 Nov '23

We invite the community to participate in the shared task we organize and consider working on data from our previous shared tasks in the scope of the CASE workshop @ EACL 2024 (https://emw.ku.edu.tr/case-2024/). Recent & Active Shared task: *T1: Climate Activism Stance and Hate Event Detection* Hate speech detection and stance detection are some of the most important aspects of event identification during climate change activism events. In the case of hate speech detection, the event is the occurrence of hate speech, the entity is the target of the hate speech, and the relationship is the connection between the two. The hate speech event has targets to which hate is directed. Identification of targets is an important task within hate speech event detection. Additionally, stance event detection is an important part of assessing the dynamics of protests and activisms for climate change. This helps to understand whether the activist movements and protests are being supported or opposed. This task will have three subtasks (i) Hate speech identification (ii) Targets of Hate Speech Identification (iii) Stance Detection. *Codalab Link:* https://codalab.lisn.upsaclay.fr/competitions/16206 <https://codalab.lisn.upsaclay.fr/competitions/16206> Registration: In order to register for the shared task, please send a request in Codalab. The organizers will approve requests on a daily basis. *GitHub Page:* https://github.com/therealthapa/case2024-climate <https://github.com/therealthapa/case2024-climate> *Timeline*: Training & Evaluation data available: Nov 1, 2023 Test data available: Nov 30, 2023 Test start: Nov 30, 2023 Test end: Jan 5, 2024 System Description Paper submissions due: Jan 12, 2024 Notification to authors after review: Jan 26, 2024 Camera ready: Jan 30, 2024 CASE Workshop: 21-22 Mar, 2024 Previous shared tasks for working on regular papers (no official competition), please see the regular paper submission timeline: PT1: MULTILINGUAL PROTEST NEWS DETECTION The performance of an automated system depends on the target event type as it may be broad or potentially the event trigger(s) can be ambiguous. The context of the trigger occurrence may need to be handled as well. For instance, the ‘protest’ event type may be synonymous with ‘demonstration’ or not in a specific context. Moreover, the hypothetical cases such as future protest plans may need to be excluded from the results. Finally, the relevance of a protest depends on the actors as in a contentious political event only citizen-led events are in the scope. This challenge becomes even harder in a cross-lingual and zero-shot setting in case training data are not available in new languages. We tackle the task in four steps and hope state-of-the-art approaches will yield optimal results. Contact person: Ali Hürriyetoğlu (ali.hurriyetoglu(a)gmail.com) Github: https://github.com/emerging-welfare/case-2022-multilingual-event PT2: EVENT CAUSALITY IDENTIFICATION Causality is a core cognitive concept and appears in many natural language processing (NLP) works that aim to tackle inference and understanding. We are interested in studying event causality in the news and, therefore, introduce the Causal News Corpus. The Causal News Corpus consists of 3,767 event sentences extracted from protest event news, that have been annotated with sequence labels on whether it contains causal relations or not. Subsequently, causal sentences are also annotated with Cause, Effect and Signal spans. Our subtasks work on the Causal News Corpus, and we hope that accurate, automated solutions may be proposed for the detection and extraction of causal events in news. Contact person: Fiona Anting Tan (tan.f(a)u.nus.edu) Github: https://github.com/tanfiona/CausalNewsCorpus PT3: MULTIMODAL HATE SPEECH EVENT DETECTION Hate speech detection is one of the most important aspects of event identification during political events like invasions. In the case of hate speech detection, the event is the occurrence of hate speech, the entity is the target of the hate speech, and the relationship is the connection between the two. Since multimodal content is widely prevalent across the internet, the detection of hate speech in text-embedded images is very important. Given a text-embedded image, this task aims to automatically identify the hate speech and its targets. This task will have two subtasks. Contact person: Surendrabikram Thapa (surendrabikram(a)vt.edu) Codalab page: https://codalab.lisn.upsaclay.fr/competitions/16203 Github: https://github.com/therealthapa/case2023_task4 Note: The organizers follows a specific timeline. Please see the Codalab page.

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PhD position in Machine Learning, Data Science and Law
by Spanakis, Jerry (DACS) 08 Nov '23

08 Nov '23

At Maastricht Law & Tech lab<https://www.maastrichtuniversity.nl/about-um/faculties/law/research/law-and…> we are looking for a PhD candidate to work on the intersection of Machine Learning, Data Science and Law. We are explicitly looking for candidates that have proven experience working on NLP and ML. See recent examples of our work [1<https://aclanthology.org/2023.eacl-main.203.pdf>,2<https://aclanthology.org/2023.acl-long.481.pdf>,3<https://arxiv.org/abs/2309.17050>] Deadline is December 10th. The full text of the position is here<https://www.academictransfer.com/en/334212/phd-candidate-position-on-machin…> and attached below. Feel free to reach out if you have questions, enquiries, etc. — Maastricht University invites applications for a fully funded PhD position focused on integrating machine learning and data science techniques with the law. JOB DESCRIPTION Law affects us all, but technology often benefits the wealthy the most. In your project, you will seek to contribute to a better understanding of how technical methods can assist in the application, interpretation, and evaluation of laws, for instance by developing innovative solutions for start-ups and ordinary citizens. Data sources may include legal texts (e.g. laws, court decisions), social media data, or computer code itself. You will work closely with industry partners and public authorities and have tangible real-world impact with your work. The ability to find innovative technical solutions to legal challenges could become a key skill of your future career be it as a researcher, entrepreneur, or engineer. Your project will connect to previous research. In prior research, we have analyzed whether AI can assist in (I) retrieving relevant legal information given a layperson’s question, (ii) detecting vendors on the Dark Web, (iii) measuring where a user’s data ends up when using Android and IOS, (iv) researching cancel culture, and (v) detecting important court decisions. You will undertake PhD research at the intersection of law and computer science under the supervision of professors of the Maastricht Law & Tech Lab. Your primary task is conducting the research for your PhD project. Your exact project will be determined in consultation with you. A small proportion of the appointment may be devoted to teaching activities, which commonly amounts to teaching activities in a period of eight weeks per year. You will be offered the opportunity to collaborate with researchers from different disciplines, including machine learning, data science, and law. You will be part of an exciting, vibrant, and quickly growing community where researchers from different disciplines meet and form interdisciplinary teams that conduct academically and societally relevant research. You will be offered the opportunity to gain insights not only on applying computational techniques, but also on law, regulation, and ethics. For this, you will be encouraged, coached, and allowed to attend courses, conferences and workshops that will add social and legal knowledge to your skillset. PhD researchers participate in the Maastricht University Graduate School of Law. REQUIREMENTS Requirements MSc degree in Computer Science, Machine Learning, or Data Science (or equivalent). Proven experience with applying machine learning, natural language processing and/or data science. Interest in learning about other disciplines, law in particular. Community-friendly team player. Excellent oral and written English communication. Optional: geek-friendly. CONDITIONS OF EMPLOYMENT As PhD candidate at Faculty of Law, you will be employed by the most international university in the Netherlands, located in the beautiful city of Maastricht. In addition, we offer you: Good employment conditions. The position is graded in scale P according to UFO profile Promovendus, with corresponding salary based on experience ranging from €2770,00 and €3539,00 gross per month (based on a full-time employment of 38 hours per week). In addition to the monthly salary, an 8.0% holiday allowance and an 8.3% year-end bonus apply. An employment contract for a period of 12 months with a scope of 1,0 FTE. At Maastricht University, the well-being of our employees is of utmost importance, we offer flexible working hours and the possibility to work partly from home if the nature of your position allows it. You will receive a monthly commuting and internet allowance for this. If you work full-time, you will be entitled to 29 vacation days and 4 additional public holidays per year, namely carnival Monday, carnival Tuesday, Good Friday, and Liberation Day. If you choose to accumulate compensation hours, an additional 12 days will be added. Furthermore, you can personalize your employment conditions through a collective labor agreement (CAO) choice model. As Maastricht University, we offer various other excellent secondary employment conditions. These include a good pension scheme with the ABP and the opportunity for UM employees to participate in company fitness and make use of the extensive sports facilities that we also offer to our students. Last but certainly not least, we provide the space and facilities for your personal and professional development. We facilitate this by offering a wide range of training programs and supporting various well-established initiatives such as 'acknowledge and appreciate'. The terms of employment at Maastricht University are largely set out in the collective labor agreement of Dutch Universities. In addition, local provisions specific to UM apply. For more information, click here.

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Release of the ICNALE Global Rating Archives
by Shin ISHIKAWA 08 Nov '23

08 Nov '23

(Sorry for cross-posting) Dear ML members We are delighted to announce the release of the ICNALE Global Rating Archives V2.0, which is the first public release version. The ICNALE GRA includes analytic/holistic ratings of L2 English learner speeches/essays by 160 raters with varied L1 and occupational backgrounds. It also includes fully edited versions of learner essays. Please download the data from the link below: https://language.sakura.ne.jp/icnale/download.html Additional info is available from the link below: https://language.sakura.ne.jp/icnale/modules.html#5 Thank you. Shin _______________________________ The ICNALE Development Team Dr. Shin ISHIKAWA (he/his) Professor of Applied Linguistics at Kobe University, Japan iskwshin(a)gmail.com

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Corpora