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PhD student position: NLP and causal inference
by Richard Johansson 09 Feb '23

09 Feb '23

The Department of Computer Science and Engineering at Chalmers University of Technology (Sweden) is looking for a PhD student in NLP for a project on text-based causal inference with applications in the social sciences. The project will be run jointly by Chalmers and the Institute for Analytical Sociology (at Linköping, Sweden). To apply, please follow the instructions in the application portal: https://web103.reachmee.com/ext/I003/304/job?site=5&lang=UK&validator=a72ae… The application deadline is February 28. For any questions about the position, please get in touch with Richard Johansson (richard.johansson(a)gu.se). Best regards, Richard Johansson

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4-year postdoctoral fellowship, University of Oslo
by Lilja Øvrelid 09 Feb '23

09 Feb '23

A position as Postdoctoral Research Fellow is available in the Language Technology Group (LTG) of the Department of Informatics (IFI), University of Oslo. The fellowship period is for a period of four (4) years. 25% of the position will be dedicated to teaching activities. For more information, please see the full announcement here: https://www.jobbnorge.no/en/available-jobs/job/237075/postdoctoral-research… The closing date is February 15th, 2023. Please do not hesitate to contact me for any further information. Best regards, Lilja Lilja Øvrelid Language Technology Group Section for Machine Learning Department of Informatics, University of Oslo

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[CfP Deadline Extension] TOIS Efficiency in Neural Information Retrieval
by Maria Maistro 09 Feb '23

09 Feb '23

Third Call for Papers - ACM Transactions on Information Systems Special Section on Efficiency in Neural Information Retrieval Full Call of Papers: https://dl.acm.org/journal/tois/calls-for-papers Overview 🧐 -------------------------- The aim of this Special Section is to engage with researchers in Information Retrieval, Natural Language Processing, and related areas and gather insight into the core challenges in measuring, reporting, and optimizing all facets of efficiency in Neural Information Retrieval (NIR) systems, including time-, space-, resource-, sample- and energy- efficiency, among other factors. This special section solicits perspectives from active researchers to advance our understanding of and to overcome efficiency challenges in NIR. In particular, researchers are encouraged to examine the ever-growing model complexity through appropriate empirical analysis, to propose models that require less data, computational resources, and energy for training and fine-tuning with similarly efficient inference, to ask if there are meaningful simplifications of the existing training processes or model architectures that lead to comparable quality, and explore a multi-faceted evaluation of NIR models from quality to all dimensions of efficiency with standardized metrics. Topics 🔍 -------------------------- We welcome submissions on the following topics, including but not limited to: * Novel NIR models that reach competitive quality but are designed to provide efficient training or inference; * Efficient NIR models for decentralized IR tasks such as conversational search; * Efficient NIR models for IR-related tasks such as question answering and recommender systems; * Efficient NIR for resource-constrained devices; * Scalability of NIR systems; * Efficient NIR for text and cross-modal search; * Strategies to optimize training or inference of existing NIR models; * Sample-efficient training of NIR models; * Efficiency-driven distillation, pruning, quantization, retraining, and transfer learning; * Empirical investigation of the complexity of existing NIR models through an analysis of quality, interpretability, robustness, and environmental impact; * Evaluation protocols for efficiency in NIR. Important Dates 🔥 -------------------------- * Open for Submissions: Aug 1, 2022 * Submissions deadline: Feb 28, 2023 * First-round review decisions: May 31, 2023 * Deadline for minor revision submissions: Jun 30, 2023 * Deadline for major revision submissions: Aug 31, 2023 * Notification of final decisions: Sept 30, 2023 * Tentative publication: 2024 Guest Editors 📚 -------------------------- * Dr. Sebastian Bruch, Pinecone, United States of America * Prof. Claudio Lucchese, Ca' Foscari University of Venice, Italy * Dr. Maria Maistro, University of Copenhagen, Denmark * Dr. Franco Maria Nardini, ISTI-CNR, Italy ——— Maria Maistro, PhD Tenure-track Assistant Professor Department of Computer Science University of Copenhagen Universitetsparken 5, 2100 Copenhagen, Denmark

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Deadline extension: 19th Workshop on Multiword Expressions (MWE 2023)
by Kilian Evang 09 Feb '23

09 Feb '23

[Apologies for cross-postings] ******************************************************************************** Call for Papers: Deadline extended 19th Workshop on Multiword Expressions (MWE 2023) Organized and sponsored by SIGLEX, the Special Interest Group on the Lexicon of the ACL Full-day workshop collocated with EACL 2023, Dubrovnik, Croatia, May 5 or 6, 2023 Hybrid (on-site & on-line) NEW: Submission deadline: February 20, 2023 NEW: Invited speakers announced (see below) NEW: Best paper award (see below) MWE 2023 website: https://multiword.org/mwe2023/ ******************************************************************************** Multiword expressions (MWEs) are word combinations that exhibit lexical, syntactic, semantic, pragmatic, and/or statistical idiosyncrasies (Baldwin & Kim 2010), such as by and large, hot dog, pay a visit and pull one's leg. The notion encompasses closely related phenomena: idioms, compounds, light-verb constructions, phrasal verbs, rhetorical figures, collocations, institutionalised phrases, etc. Their behaviour is often unpredictable; for example, their meaning often does not result from the direct combination of the meanings of their parts. Given their irregular nature, MWEs often pose complex problems in linguistic modelling (e.g. annotation), NLP tasks (e.g. parsing), and end-user applications (e.g. natural language understanding and MT), hence still representing an open issue for computational linguistics (Constant et al. 2017). For almost two decades, modelling and processing MWEs for NLP has been the topic of the MWE workshop organised by the MWE section of SIGLEX in conjunction with major NLP conferences since 2003. Impressive progress has been made in the field, but our understanding of MWEs still requires much research considering their need and usefulness in NLP applications. This is also relevant to domain-specific NLP pipelines that need to tackle terminologies most often realised as MWEs. Following previous years, for this 19th edition of the workshop, we identified the following topics on which contributions are particularly encouraged: MWE processing and identification in specialized languages and domains: Multiword terminology extraction from domain-specific corpora (Bonin et al. 2010) is of particular importance to various applications, such as MT (Semmar & Laib, 2017), or for the identification and monitoring of neologisms and technical jargon (Chatzitheodorou et al, 2021). We expect approaches that deal with the processing of MWEs as well as the processing of terminology in specialised domains can benefit from each other. MWE processing to enhance end-user applications: MWEs have gained particular attention in end-user applications, including MT (Zaninello & Birch 2020; Han et al. 2021, 2022), simplification (Kochmar et al. 2020), language learning and assessment (Paquot et al. 2019; Christiansen & Arnon 2017), social media mining (Maisto et al. 2017), and abusive language detection (Zampieri et al. 2020; Caselli et al. 2020). We believe that it is crucial to extend and deepen these first attempts to integrate and evaluate MWE technology in these and further end-user applications. MWE identification and interpretation in pre-trained language models: Most current MWE processing is limited to their identification and detection using pre-trained language models, but we still lack understanding about how MWEs are represented and dealt with therein (Nedumpozhimana & Kelleher 2021; Garcia et al. 2021, Fakharian & Cook 2021), how to better model the compositionality of MWEs from semantics (Moreau et al. 2018). Now that NLP has shifted towards end-to-end neural models like BERT, capable of solving complex tasks with little or no intermediary linguistic symbols, questions arise about the extent to which MWEs should be implicitly or explicitly modelled (Shwartz & Dagan, 2019). MWE processing in low-resource languages: The PARSEME shared tasks (Ramisch et al. 2020; 2018; Savary et al. 2017), among others, have fostered significant progress in MWE identification, providing datasets that include low-resource languages, evaluation measures, and tools that now allow fully integrating MWE identification into end-user applications. A few efforts have recently explored methods for the automatic interpretation of MWEs (Bhatia, et al. 2018; 2017), and their processing in low-resource languages (Liu & Wang 2020; Kumar et al. 2017). Resource creation and sharing should be pursued in parallel with the development of methods able to capitalize on small datasets (Han et al. 2020). Through this workshop, we would like to bring together and encourage researchers in various NLP subfields to submit MWE-related research, so that approaches that deal with processing of MWEs including processing for low-resource languages and for various applications can benefit from each other. We also intend to consolidate the converging effects of previous joint workshops LAW-MWE-CxG 2018, MWE-WN 2019 and MWE-LEX 2020, the joint MWE-WOAH panel in 2021, and the MWE-SIGUL 2022 joint session, extending our scope to MWEs in e-lexicons and WordNets, MWE annotation, as well as grammatical constructions. Correspondingly, we call for papers on research related (but not limited) to MWEs and constructions in: Computationally-applicable theoretical work in psycholinguistics and corpus linguistics; Annotation (expert, crowdsourcing, automatic) and representation in resources such as corpora, treebanks, e-lexicons, and WordNets (also for low-resource languages); Processing in syntactic and semantic frameworks (e.g. CCG, CxG, HPSG, LFG, TAG, UD, etc.); Discovery and identification methods, including for specialized languages and domains such as clinical or biomedical NLP; Interpretation of MWEs and understanding of text containing them; Language acquisition, language learning, and non-standard language (e.g. tweets, speech); Evaluation of annotation and processing techniques; Retrospective comparative analyses from the PARSEME shared tasks; Processing for end-user applications (e.g. MT, NLU, summarisation, language learning, etc.); Implicit and explicit representation in pre-trained language models and end-user applications; Evaluation and probing of pre-trained language models; Resources and tools (e.g. lexicons, identifiers) and their integration into end-user applications; Multiword terminology extraction; Adaptation and transfer of annotations and related resources to new languages and domains including low-resource ones. Shared Task We do not have a shared task this year, but a new release of the PARSEME corpus of verbal MWEs is currently underway. We encourage submission of research papers that include analyses of the new edition of the PARSEME data and improvements over the results for PARSEME 2020 shared task as well as SemEval 2022 task 2 on idiomaticity prediction. *** Special Track on MWEs in Clinical NLP *** Pursuing the MWE Section’s tradition of synergies with other communities, this year, we are organizing a joint session with the Clinical NLP workshop for shared papers/poster presentations. Since clinical texts contain an important amount of multiword expressions (e.g. medical terms or domain-specific collocations), a joint session is deemed beneficial for both communities. The goal is to foster future synergies that could address scientific challenges in the creation of resources, models and applications to deal with multiword expressions and related phenomena in the specialised domain of ClinicalNLP. Submissions describing research on MWEs in the specialized domain of ClinicalNLP, especially introducing new datasets or new tools and resources, are welcome. Papers accepted in this track will have the option to present their work in the Clinical NLP workshop at ACL 2023 as well, after being presented at MWE 2023. Invited Speakers We are looking forward to invited talks by two amazing speakers: Leo Wanner, Universitat Pompeu Fabra TBD Best paper award All full papers in the workshop will be considered by the program committee for a best paper award. The decision will be announced in the closing session. Submission formats The workshop invites two types of submissions: archival submissions that present substantially original research in both long paper format (8 pages + references) and short paper format (4 pages + references). non-archival submissions of abstracts describing relevant research presented/published elsewhere which will not be included in the MWE proceedings. Paper submission and templates Papers should be submitted via the workshop's START submission page (https://softconf.com/eacl2023/mwe2023/). Please choose the appropriate submission format (archival/non-archival). Archival papers with existing reviews will also be accepted through the ACL Rolling Review. Submissions must follow the ACL 2023 stylesheet. Archival papers with existing reviews from ACL Rolling Review will also be considered. A paper may not be simultaneously under review through ARR and MWE. A paper that has or will receive reviews through ARR may not be submitted for review to MWE. Important Dates Paper submission: February 20, 2023 ARR paper commitment: March 6, 2023 Notification of acceptance: March 13, 2023 Camera-ready papers due: March 27, 2023 Workshop: May 5 or 6, 2023 All deadlines are at 23:59 UTC-12 (Anywhere on Earth). Organizing Committee Program chairs: Marcos Garcia, Voula Giouli, Lifeng Han, Shiva Taslimipoor Publication chair: Archna Bhatia Publicity chair: Kilian Evang Anti-harassment policy The workshop follows the ACL anti-harassment policy. Contact For any inquiries regarding the workshop, please send an email to the Organizing Committee at mweworkshop2023(a)googlegroups.com.

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A Fully Funded PhD Position in Natural Language Processing
by Xiaodan Zhu 09 Feb '23

09 Feb '23

A fully-funded PhD position in Natural Language Processing and Machine Learning is available at the Text Analytics and Machine Learning Group led by Prof. Xiaodan Zhu (www.xiaodanzhu.com) at the Department of Electrical and Computer Engineering at Queen’s University Canada. The student will also be affiliated with the Ingenuity Labs Research Institute at Queen’s University (https://ingenuitylabs.queensu.ca/). Deadline for application: Apr. 1, 2023 Starting date: Sep. 1, 2023 or Jan. 1, 2024 Contact: xiaodan.zhu(a)queensu.ca Webpage: www.xiaodanzhu.com Application: https://engineering.queensu.ca/future-students/graduate/ About Queen’s University: https://www.queensu.ca/

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CfP: 5th Workshop on Research in Computational Linguistic Typology and Multilingual NLP (SIGTYP 2023 @EACL)
by Kat Vylomova 09 Feb '23

09 Feb '23

We are inviting your submissions to the 5th Workshop on Research in Computational Linguistic Typology and Multilingual NLP (SIGTYP 2023) which will be held at EACL 2023 (May 5 or 6, 2023 Dubrovnik, Croatia). The submission deadline is **February 13**. Submission link: https://openreview.net/group?id=eacl.org/EACL/2023/Workshop/SIGTYP WORKSHOP DESCRIPTION The aim of the 5th edition of SIGTYP workshop is to act as a platform and a forum for the exchange of information between typology-related research, multilingual NLP, and other research areas that can lead to the development of truly multilingual NLP methods. The workshop is specifically aimed at raising awareness of linguistic typology and its potential in supporting and widening the global reach of multilingual NLP, as well as at introducing computational approaches to linguistic typology. It will foster research and discussion on open problems, not only within the active community working on cross- and multilingual NLP but also inviting input from leading researchers in linguistic typology. In 2023, we would like to continue following this direction of research with a special focus on bringing technology to foster documentation of under-described languages. SIGTYP is the first dedicated venue for typology-related research and its integration in multilingual NLP. Appropriate topics include (but are not limited to) the following as they relate to the areas of the workshop: -- Integration of typological features in language transfer and joint multilingual learning. In addition to established techniques such as “selective sharing”, are there alternative ways to encoding heterogeneous external knowledge in machine learning algorithms? -- Development of unified taxonomy and resources. Building universal databases and models to facilitate understanding and processing of diverse languages. -- Automatic inference of typological features. The pros and cons of existing techniques (e.g. heuristics derived from morphosyntactic annotation, propagation from features of other languages, supervised Bayesian and neural models) and discussion on emerging ones. -- Typology and interpretability. The use of typological knowledge for interpretation of hidden representations of multilingual neural models, multilingual data generation and selection, and typological annotation of texts. -- Improvement and completion of typological databases. Combining linguistic knowledge and automatic data-driven methods towards the joint goal of improving the knowledge on cross-linguistic variation and universals. -- Linguistic diversity and universals. Challenges of cross-lingual annotation. Which linguistic phenomena or categories should be considered universal? How should they be annotated? -- Bringing technology to document under-described languages. Improving model performance and documentation of under-resourced languages using typological databases, multilingual models and data from high-resource languages. -- Cognate and Derivative Detection for Low-Resourced Languages. This year’s edition will include a shared task: “Cognate and Derivative Detection for Low-Resourced Languages”; more details can be found here: https://github.com/sigtyp/ST2023. IMPORTANT DATES (all deadlines are 23:59 AoE) — February 13, 2023: Paper submission deadline — March 13, 2023: Notification of acceptance — March 27, 2023: Camera-ready deadline — May 5 or 6, 2023: Workshop SUBMISSIONS We invite both extended abstract submissions (non-archival) and general paper submissions (archival). The accepted submissions will be presented at the workshop, providing new insights and ideas. Extended abstracts should describe already published work or work in progress and should not exceed two (2) pages. This way, we will not discourage researchers from preferring main conference proceedings, at the same time ensuring that interesting and thought-provoking research is presented at the workshop. For general (archival) submissions we accept both long and short papers. Short papers should not exceed four (4) pages, long papers should not exceed eight (8) pages papers. Unlimited additional pages are allowed for the references section in all submission types. Submissions should be anonymous, without authors or an acknowledgement section; self-citations should appear in third person. Submissions must follow the EACL 2023 stylesheet https://2023.eacl.org/calls/styles/; both long and short paper submissions must follow the two-column format of ACL proceedings. All submissions must be in PDF format. These should be submitted via OpenReview: https://openreview.net/group?id=eacl.org/EACL/2023/Workshop/SIGTYP. We are accepting all papers from EACL Findings that are relevant to SIGTYP. Contact us via sigtyp AT gmail DOT com if you would like to present your EACL Findings paper at SIGTYP 2023! ORGANIZING COMMITTEE Koustava Goswami, Alexey Sorokin, Ritesh Kumar, Andrey Shcherbakov, Edoardo M. Ponti, Saliha Muradoğlu, Lisa Beinborn, Ryan Cotterell, Kat Vylomova ANTI-HARASSMENT POLICY The workshop follows the ACL anti-harassment policy: https://www.aclweb.org/adminwiki/index.php?title=Anti-Harassment_Policy. CONTACT For any inquiries regarding the workshop, please send an email to the Organizing Committee at sigtyp(a)gmail.com

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JOB: Wissenschaftliche*r Mitarbeiter*in (m/w/d) im Bereich der digitalen Text- und Literaturwissenschaft
by Berenike Herrmann 08 Feb '23

08 Feb '23

Dear all, please distribute to potential candidates. Job ad in German, suitable for uni graduates (BA/MA e.g. in Digital Humanities / Data Science / Media Studies / Computer Linguistics) and people with XML-expertise who are interested in a part-time opportunity in the field of XML-database implementation ( 50%, one year ) in a project based in Bielefeld, Germany. Basic command of German necessary. I’d be happy to answer questions! Many thanks! Best, Berenike ############### An der Universität Bielefeld ist eine Stelle Wissenschaftliche*r Mitarbeiter*in (m/w/d) im Bereich der digitalen Text- und Literaturwissenschaft für das Projekt „KOLIMO+. Optimierung und Offene Publikation des Korpus der literarischen Moderne“ zum nächstmöglichen Termin zu besetzen. Das Projekt wird von Prof. Dr. Berenike Herrmann im Bereich Germanistik geleitet. Das von der NFDI Text+ geförderte Drittmittelprojekt zielt darauf ab, ein bestehendes Korpus-Datenset aus literarischen Texten nach philologischen und datenwissenschaftlichen Standards gemäß der FAIR- und CARE-Prinzipien aufzubereiten und langfristig interoperabel zu machen, indem es im Rahmen der Text+-Services zur Verfügung gestellt wird. Neben der Überführung der eXist-db Ressource in die TextGrid-Architektur und der sichtbaren Publikation als Use-Case soll es in Zusammenarbeit mit dem Text+-Kompetenzzentrum eine Fallstudie für die Implementierung einer API für das TextGrid-Repository bilden. Zentrales Aufgabenfeld ist die Entwicklung und Publikation der XML-Datenbank KOLIMO+ in Zusammenarbeit mit NFDI Text+ Kompetenzzentrum SUB Göttingen. Gesucht wird wissenschaftliche*r Mitarbeiter*in (m/f/d) mit BA oder MA-Abschluss in Digital Humanities / Data Science / Informatik / Medienwissenschaft / Computerlinguistik / Germanistik (oder anderem einschlägigen Fach) mit Erfahrung im Umgang mit X-Technologien (XML, XSLT, XPath) und XML-Datenbanken, Kenntnis mindestens einer Programmiersprache (z. B. R, Java oder Python) und Erfahrungen mit Application Programming Interfaces (API) oder die Fähigkeit und Bereitschaft, sich diese schnell anzueignen Je nach persönlicher Qualifikation bis zu E13 TV-L befristet auf 1 Jahr (§ 2 Abs. 1 WissZeitVG; entsprechend den Vorgaben des WissZeitVG und des Vertrages über gute Beschäftigungsbedingungen kann sich im Einzelfall eine abweichende Vertragslaufzeit ergeben) | Teilzeit 50 % Interessiert? Dann freuen wir uns über Ihre aussagekräftige Bewerbung. Bitte übersenden Sie uns hierfür Ihre vollständigen Bewerbungsunterlagen unter Angabe der Kennziffer Wiss23053 per E-Mail in einem pdf-Dokument an andrea.schrottenloher(a)unibielefeld.de oder per Post an die angegebene Adresse. Bewerbungsfrist: 01.03.2023 Link zur Ausschreibung: https://uni-bielefeld.hr4you.org/job/view/2223/wissenschaftliche-r-mitarbei… Wir freuen uns auf Bewerbungen! Mit freundlichen Grüßen, Berenike Herrmann Prof. Dr. Berenike Herrmann German Literature / Digital Humanities Bielefeld University https://jberenike.github.io/ Vice Chair SCC Collections NFDI text+ (National Research Data Infrastructure, Consortium text+) Speaker BiLinked CoP Data Literacy Principal Investigator SNF-Project “High Mountains Low Arousal? Distant Reading Topographies of Sentiment in German Swiss Novels in the early 20th Century” Principal Investigator SFB1288 Project “Vergleichspraktiken in der Genese, Verstetigung und Transformation von ‘Nationalliteratur’. Der Fall Deutschschweiz”

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First Call for Task Proposals for SemEval-2024
by Atul K. Ojha 08 Feb '23

08 Feb '23

Apologies for cross-posting. ------------------------------------------------------------------------- ------ We invite proposals for tasks to be run as part of SemEval-2024 <https://semeval.github.io/SemEval2024/>. SemEval (the International Workshop on Semantic Evaluation) <https://semeval.github.io/>is an ongoing series of evaluations of computational semantics systems, organized under the umbrella of SIGLEX <https://siglex.org/>, the Special Interest Group on the Lexicon of the Association for Computational Linguistics. SemEval tasks explore the nature of meaning in natural languages: how to characterize meaning and how to compute it. This is achieved in practical terms, using shared datasets and standardized evaluation metrics to quantify the strengths and weaknesses and possible solutions. SemEval tasks encompass a broad range of semantic topics from the lexical level to the discourse level, including word sense identification, semantic parsing, coreference resolution, and sentiment analysis, among others. For SemEval-2024 <https://semeval.github.io/SemEval2024/cft>, we welcome any task that can test an automatic system for the semantic analysis of text, which could be an intrinsic semantic evaluation or an application-oriented evaluation. We especially encourage tasks for languages other than English, cross-lingual tasks, and tasks that develop novel applications of computational semantics. See the websites of previous editions of SemEval to get an idea about the range of tasks explored, SemEval-2022 <https://semeval.github.io/SemEval2022/> and SemEval-2023 <https://semeval.github.io/SemEval2023/>. We strongly encourage proposals based on pilot studies that have already generated initial data, as this can provide concrete examples and can help to foresee the challenges of preparing the full task. In the event of receiving many proposals, preference will be given to proposals that have already run a pilot study. In case you are not sure whether a task is suitable for SemEval, please feel free to get in touch with the SemEval organizers at semevalorganizers(a)gmail.com to discuss your idea. === Task Selection === Task proposals will be reviewed by experts, and reviews will serve as the basis for acceptance decisions. Everything else being equal, more innovative new tasks will be given preference over task reruns. Task proposals will be evaluated on: - Novelty: Is the task on a compelling new problem that has not been explored much in the community? Is the task a rerun, but covering substantially new ground (new subtasks, new types of data, new languages, etc.)? - Interest: Is the proposed task likely to attract a sufficient number of participants? - Data: Are the plans for collecting data convincing? Will the resulting data be of high quality? Will annotations have meaningfully high inter-annotator agreements? Have all appropriate licenses for use and re-use of the data after the evaluation been secured? Have all international privacy concerns been addressed? Will the data annotation be ready on time? - Evaluation: Is the methodology for evaluation sound? Is the necessary infrastructure available or can it be built in time for the shared task? Will research inspired by this task be able to evaluate in the same manner and on the same data after the initial task? - Impact: What is the expected impact of the data in this task on future research beyond the SemEval Workshop? === New Tasks vs. Task Reruns === We welcome both new tasks and task reruns. For a new task, the proposal should address whether the task would be able to attract participants. Preference will be given to novel tasks that have not received much attention yet. For reruns of previous shared tasks (whether or not the previous task was part of SemEval), the proposal should address the need for another iteration of the task. Valid reasons include: a new form of evaluation (e.g. a new evaluation metric, a new application-oriented scenario), new genres or domains (e.g. social media, domain-specific corpora), or a significant expansion in scale. We further discourage carrying over a previous task and just adding new subtasks, as this can lead to the accumulation of too many subtasks. Evaluating on a different dataset with the same task formulation, or evaluating on the same dataset with a different evaluation metric, typically should not be considered a separate subtask. === Task Organization === We welcome people who have never organized a SemEval task before, as well as those who have. Apart from providing a dataset, task organizers are expected to: - Verify the data annotations have sufficient inter-annotator agreement - Verify licenses for the data to allow its use in the competition and afterwards. In particular, text that is publicly available online is not necessarily in the public domain; unless a license has been provided, the author retains all rights associated with their work, including copying, sharing and publishing. For more information, see: https://creativecommons.org/faq/#what-is-copyright-and-why-does-it-matter - Resolve any potential security, privacy, or ethical concerns about the data - Make the data available in a long-term repository under an appropriate license, preferably using Zenodo: https://zenodo.org/communities/semeval/ - Provide task participants with format checkers and standard scorers. - Provide task participants with baseline systems to use as a starting point (in order to lower the obstacles to participation). A baseline system typically contains code that reads the data, creates a baseline response (e.g. random guessing, majority class prediction), and outputs the evaluation results. Whenever possible, baseline systems should be written in widely used programming languages and/or should be implemented as a component for standard NLP pipelines. - Create a mailing list and website for the task and post all relevant information there. - Create a CodaLab or other similar competition for the task and upload the evaluation script. - Manage submissions on CodaLab or a similar competition site. - Write a task description paper to be included in SemEval proceedings, and present it at the workshop. - Manage participants’ submissions of system description papers, manage participants’ peer review of each others’ papers, and possibly shepherd papers that need additional help in improving the writing. - Review other task description papers. *=== Important dates ===* *- Task proposals due April 03, 2023 (Anywhere on Earth)- Task selection notification May 22, 2023* === Preliminary timetable === - Sample data ready July 15, 2023 - Training data ready September 1, 2023 - Evaluation data ready December 1, 2023 (internal deadline; not for public release) - Evaluation starts January 10, 2024 - Evaluation end by January 31, 2024 (latest date; task organizers may choose an earlier date) - Paper submission due February 2024 - Notification to authors on March 2024 - Camera-ready due April 2024 - SemEval workshop Summer 2024 (co-located with a major NLP conference) Tasks that fail to keep up with crucial deadlines (such as the dates for having the task and CodaLab website up and dates for uploading samples, training, and evaluation data) may be cancelled at the discretion of SemEval organizers. While consideration will be given to extenuating circumstances, our goal is to provide sufficient time for the participants to develop strong and well-thought-out systems. Cancelled tasks will be encouraged to submit proposals for the subsequent year’s SemEval. To reduce the risk of tasks failing to meet the deadlines, we are unlikely to accept multiple tasks with overlap in the task organizers. === Submission Details === The task proposal should be a self-contained document of no longer than 3 pages (plus additional pages for references). All submissions must be in PDF format, following the ACL template <https://github.com/acl-org/acl-style-files>. Each proposal should contain the following: - Overview - Summary of the task - Why this task is needed and which communities would be interested in participating - Expected impact of the task - Data & Resources - How the training/testing data will be produced. Please discuss whether existing corpora will be re-used. - Details of copyright, so that the data can be used by the research community both during the SemEval evaluation and afterwards - How much data will be produced - How data quality will be ensured and evaluated - An example of what the data would look like - Resources required to produce the data and prepare the task for participants (annotation cost, annotation time, computation time, etc.) - Assessment of any concerns with respect to ethics, privacy, or security (e.g. personally identifiable information of private individuals; potential for systems to cause harm) - Pilot Task (strongly recommended) - Details of the pilot task - What lessons were learned and how these will impact the task design - Evaluation - The evaluation methodology to be used, including clear evaluation criteria - For Task Reruns - Justification for why a new iteration of the task is needed (see criteria above) - What will differ from the previous iteration - Expected impact of the rerun compared with the previous iteration - Task organizers - Names, affiliations, email addresses - (optional) brief description of relevant experience or expertise - (if applicable) years and task numbers, of any SemEval tasks you have run in the past Proposals will be reviewed by an independent group of area experts who may not have familiarity with recent SemEval tasks, and therefore all proposals should be written in a self-explanatory manner and contain sufficient examples. The submission webpage is: https://openreview.net/group?id=aclweb.org/ACL/2023/Workshop/SemEval === Chairs === Ritesh Kumar, Dr. Bhimrao Ambedkar University Atul Kr. Ojha, SFI Insight Centre for Data Analytics, DSI, University of Galway A. Seza Doğruöz, Ghent University Giovanni Da San Martino, University of Padua Harish Tayyar Madabushi, The University of Bath Contact: semevalorganizers(a)gmail.com

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Corpus of events with geolocation
by Hristo Tanev 08 Feb '23

08 Feb '23

Dear Corpora members, I am searching for a corpus of conflict events, annotated with geolocation. I am well aware of ACLED, but it's event descriptions are created manually. Ideally it should be a corpus of news or social media messages containing descriptions of armed conflicts and protests and annotated with geographical information. If any of you is aware of a similar resource and directs me to it, I will be very thankful! Hristo Tanev, JRC, Ispra

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Open full-time permanent positions -- NLP & argumentation
by elena_cabrio 08 Feb '23

08 Feb '23

Dear all, To develop its NLP activity, the WIMMICS team (https://team.inria.fr/wimmics/) at Inria Sophia Antipolis and Université Côte d’Azur (France) is looking for candidates for full-time permanent researcher and full-time assistant professor positions in natural language processing (NLP) and argumentation to join us in the beautiful French Riviera. We welcome profiles for both junior and senior positions. A PhD and at least one post-doctoral experience in NLP or ML for NLP are required for junior positions, whereas a longer track record is required for senior positions. Inria is the French national research institute for computer science and applied mathematics. World-class research, and, where relevant, transfer to industry and start-up creation are its main missions. The I3S Laboratory of Université Côte d’Azur and CNRS is one of the largest information and communication science laboratories in the French Riviera. The NLP research in WIMMICS focuses on NLP and argumentation, at the crossroad between machine and deep learning, linguistics and argumentation theory. Joining the NLP researchers of WIMMICS means working in a highly international and inclusive team within an exceptional scientific environment. The team is also part of the national Interdisciplinary Institute for Artificial Intelligence 3IA Côte d’Azur (https://3ia.univ-cotedazur.eu/). Junior permanent researchers are eligible to receive a welcome package including a full PhD grant and expenses. If you are interested, please contact Elena Cabrio (elena.cabrio(a)univ-cotedazur.fr) and Serena Villata (serena.villata(a)univ-cotedazur.fr) by February 15th, 2023. All the best, Elena Cabrio (Full professor - Université Côte d’Azur) Serena Villata (Research director - CNRS)

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