- Corpora - ELRA lists

2-year postdoc in NLP/data science
by Nina Tahmasebi 31 Oct '22

31 Oct '22

Dear colleagues, We are hiring a second postdoc! Do you have a lot of drive and are interested in getting engaged in a vibrant, new research field with the chance to work with some of the best in the field? We are announcing one or more 2-year postdoc positions in identification and analysis of lexical semantic change using computational models applied to diachronic texts. Application deadline November 15! Our languages change over time. As a consequence, words may look the same, but have different meanings at different points in time, a phenomenon called lexical semantic change (LSC). To facilitate interpretation, search, and analysis of old texts, we build computational methods for automatic detection and characterization of LSC from large amounts of text. Our outputs will be used by the lexicographic R&D unit that compiles the Swedish Academy dictionaries, as well as by researchers from the humanities and social sciences that include textual analysis as a central methodological component. We are looking for a researcher with a phd in a relevant area (for example, language technology/natural language processing, computer science, complex systems, mathematics, linguistics with experience in computational methods, or informational sciences). The Change is Key! program and the Towards Computational Lexical Semantic Change Detection research project offer a vibrant research environment for this exciting and rapidly growing cutting-edge research field in NLP. There is a unique opportunity to contribute to the field of LSC, but also to humanities and social sciences through our active collaboration with international researchers in historical linguistics, analytical sociology, gender studies, conceptual history, and literary studies. Come join us! https://web103.reachmee.com/ext/I005/1035/job?site=7&lang=UK&validator=9b89… -- Nina N. Tahmasebi, Associate Professor Språkbanken • Change is Key! +46 (0) 31 786 6953 nina.tahmasebi(a)gu.se http://spraakbanken.gu.se/eng/personal/nina https://changeiskey.org/ https://languagechange.org/ http://tahmasebi.se/ https://gu-se.zoom.us/my/ninatahmasebi “If at first, the idea is not absurd, then there is no hope for it” -Albert Einstein.

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Recruiting graduate and undergraduate summer research interns for Data-Driven Accessibility at Microsoft Research
by Hal Daume 31 Oct '22

31 Oct '22

Dear corpora readers: We -- Danielle Bragg, Alex Lu, and Hal Daumé III -- are looking to hire research interns to work on data-driven accessibility research projects, alongside leading researchers and engineers in the field. We are recruiting both graduate research interns and undergraduate research interns for Summer 2023. (ASL recruitment video: https://youtu.be/Gb-8CTpKxhU.) Our team takes a human-centered and data-driven approach to advancing the state of accessible technologies. Recent work has focused on data collection methods, sign language modeling, understanding concerns and perspectives of user communities, and building novel apps and experiences. You can learn more about some of the team’s recent efforts at the data-driven accessibility systems page <https://www.microsoft.com/en-us/research/project/data-driven-accessibility-…>. These positions sit within Microsoft Research New York City, with opportunities to collaborate with Microsoft Research New England and others across the company. Our team is highly interdisciplinary and offers the opportunity to interact with diverse researchers. For graduate students, please apply (short research statement and two letters) at the Research Intern Portal <https://careers.microsoft.com/us/en/job/1483492/Research-Intern-Data-Driven…> . For undergraduate students, please apply to the MSR Undergraduate Research Internship and mention one or more of us by name (CV, 2-3 reference letters, and two essays) at the Undergraduate Research Intern Portal <https://www.microsoft.com/en-us/research/academic-program/undergraduate-res…> . Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you need assistance and/or a reasonable accommodation due to a disability during the application or the recruiting process, please send a request via the Accommodation request form <https://careers.microsoft.com/us/en/accommodationrequest>. Sincerely, Danielle Bragg, Alex Lu, and Hal Daumé III

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CFP: AAAI-2023 Workshop On Multimodal AI For Financial Forecasting
by puneetm＠umd.edu 31 Oct '22

31 Oct '22

Call for Paper: AAAI-2023 Workshop On Multimodal AI For Financial Forecasting Venue: AAAI 2023 Location: Washington DC, USA Workshop Date: Monday, 13 February 2023 Submission deadline: December 23, 2022 Submission Site: https://easychair.org/my/conference?conf=muffinaaai2023 Workshop Website: https://muffin-aaai23.github.io/ Abbreviated Title: Muffin-AAAI2023 Contact Email: muffin-aaai23(a)googlegroups.com Primary Contact: Puneet Mathur Overview Financial forecasting is an essential task that helps investors make sound investment decisions and wealth creation. With increasing public interest in trading stocks, cryptocurrencies, bonds, commodities, currencies, crypto coins and non-fungible tokens (NFTs), there have been several attempts to utilize unstructured data for financial forecasting. Unparalleled advances in multimodal deep learning have made it possible to utilize multimedia such as textual reports, news articles, streaming video content, audio conference calls, user social media posts, customer web searches, etc for identifying profit creation opportunities in the market. E.g., how can we leverage new and better information to predict movements in stocks and cryptocurrencies well before others? However, there are several hurdles towards realizing this goal - (1) large volumes of chaotic data, (2) combining text, audio, video, social media posts, and other modalities is non-trivial, (3) long context of media spanning multiple hours, days or even months, (4) user sentiment and media hype-driven stock/crypto price movement and volatility, (5) difficulties with traditional statistical methods (6) misinformation and non-interpretability of financial systems leading to massive losses and bankruptcies. At the AAAI-2023 Workshop on Multimodal AI for Financial Forecasting (Muffin@AAAI2023), we aim to bring together researchers from natural language processing, computer vision, speech recognition, machine learning, statistics, and quantitative trading communities to expand research on the intersection of AI and financial time series forecasting. We will also organize 2 shared tasks in this workshop – (1) Stock Price and Volatility Prediction post-Monetary Conference Calls and (2) Cryptocurrency Bubble Detection. This workshop will hold a research track and a shared task track. The research track aims to explore recent advances and challenges of multimodal AI for finance. As this topic is an inherently multi-modal subject, researchers from artificial intelligence, computer vision, speech processing, natural language processing, data mining, statistics, optimization, and other fields are invited to submit papers on recent advances, resources, tools, and challenges on the broad theme of Multimodal AI for finance. The topics of the workshop include but are not limited to the following: Transformer models / Self-supervised / Transfer Learning on Financial Data Machine Learning for Finance Natural Language Processing and Speech Applications for Finance Conversational dialogue modeling for Financial Conference Calls Social media and User NLP for Finance Entity extraction and linking, Named-entity recognition, information extraction, relationship extraction, and ontology learning in financial documents Financial Document Processing Multi-modal financial knowledge discovery Financial Event detection from Multimedia Visual-linguistic learning for financial video analysis Video understanding (human behavior cognition, topic mining, facial expression detection, emotion detection, deception detection, gait and posture analysis, etc.) Data annotation, acquisition, augmentation, and feature engineering, for financial/time-series analysis Bias analysis and mitigation in financial models and data Statistical Modeling for Time Series Forecasting Interpretability and explainability for financial AI models Privacy-preserving AI for finance All papers will be double-blind peer-reviewed. Muffin workshop accepts both long papers and short papers: Short Paper: Up to 4 pages of content including the references. Upon acceptance, the authors are provided with 1 more page to address the reviewer's comments. Long Paper: Up to 8 pages of content including the references. Upon acceptance, the authors are provided with 1 more page to address the reviewer's comments. Shared Task Track: Participants are invited to take part in shared tasks: (1) Financial Prediction from Conference Call Videos and (2) Cryptocurrency Bubble Detection. Participants are invited to submit a system paper of 4-8 pages of content including the references. Important Dates Paper submission deadline: December 23, 2022 Acceptance notification: January 5, 2023 Camera-ready submission: January 15, 2023 Muffin workshop at AAAI 2023: Feb 13, 2022 All deadlines are “anywhere on earth” (UTC-12) About the Shared Task The Multimodal AI for Finance Forecasting (Muffin) workshop will host two shared tasks on challenging multimodal financial forecasting problems using artificial intelligence. Follow this link for details on shared tasks: https://muffin-aaai23.github.io/shared_task.html Task-1: Financial Prediction from Conference Call Videos Monetary policy calls (MPC) provide important insights into the actions taken by a country’s central bank on economic goals related to inflation, employment, prices, and interest rates. Investors and analysts critically analyze these video calls to forecast prices of the stock market, treasury bonds, gold, and currency exchange rates post the conference call. Prior works in the NLP literature have looked at what is being said during press conferences although there is a greater need to focus on how it is being said. The use of multimodal (visual+textual+audio) input to answer this question has been largely limited. Non-verbal behavioral cues from conference videos such as eye movements, facial expressions, postures, gaits, the complexity of language, vocal tone, and facial expressions of the speakers may reflect emotions that subjects may not express through words and have been found to be strongly correlated with enhanced trading activities in the financial markets. Interpreting and extracting information from financial conference calls reveals difficult challenges such as (1) Gap in current multimodal AI methods for simultaneously leveraging visual, vocal, and verbal modalities; (2) Long length of videos (50min to 1 hour) with multi-page text transcripts (3) Need to explore few-shot, semi-supervised, and self-supervised methods due to limited training data; (4) Large variability in conference calls across geographies due to different speakers, demographics, and economic conditions causing unintended bias. To this end, we curated a dataset of video conference calls from 2009 to 2022 released by central banks of 6 major English-speaking economies - USA, Canada, European Union, United Kingdom, New Zealand, and South Africa. The data has been processed to extract video frames, audio recordings, and utterance-aligned text transcripts. The task is to predict the volatility and price movement of stock market indices, gold, currency exchange rates, and bond prices T days after a conference call. We provide a cumulative of 25K data points split across training/development/testing for experimentation. Relevant research paper: [1] MONOPOLY: Financial Prediction from MONetary POLicY Conference Videos Using Multimodal Cues Task 2: Cryptocurrency Bubble Detection on Social Media Cryptocurrency trading presents a new investment opportunity for maximizing profits. The rising ubiquity of speculative trading of cryptocurrencies over social media leads to rapid escalation and crash of price in a short period of time, also called bubbles, causing investment losses and bankruptcy. These crypto bubbles are strongly tied to user sentiment and social media usage as opposed to conventional value-driven stocks and equities. Such financial bubbles are often a result of social media hype and the intensity of contagion among users, rendering both conventional statistical models and contemporary ML models weak as they are not built to deal with large volumes of unstructured, user-generated text on social media. In order to identify and safeguard against such bubbles, we formulate the CryptoBubbles Detection Challenge - a novel multi-span prediction task over future days of time series price data for crypto assets. We have curated a dataset of the 50 most traded crypto coins by volume from the top 9 crypto exchanges such as Binance, Gatio, etc to obtain a time series of prices for 450+ crypto assets over five years accompanied by over 2.4 million related tweets. Relevant research paper: [2] Cryptocurrency Bubble Detection: A New Stock Market Dataset, Financial Task & Hyperbolic Models

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Call for Workshops and Tutorials at the Fourth Biennial Conference on Language, Data and Knowledge, LDK 2023
by Ana Ostroški Anić 30 Oct '22

30 Oct '22

[apologies for cross-posting] ============== Call for Workshops and Tutorials @ Fourth Conference on Language, Data and Knowledge (LDK2023) Date: 13 September 2023 (Workshop/Tutorials day), 14–15 September 2023 (Main Conference) Location: Vienna, Austria Website: http://2023.ldk-conf.org Submission Deadline: 19 December 2022 Submissions via: EasyChair ============== We are inviting proposals for workshops and tutorials to be held on September 13, 2023 in conjunction with the fourth biennial conference on Language, Data and Knowledge (LDK 2023) in Vienna, Austria. Building upon the success of the previous events held in Galway, Ireland in 2017, in Leipzig, Germany in 2019, and in Zaragoza, Spain in 2021, this conference will bring together researchers from across disciplines concerned with the acquisition, curation and use of language data in the context of data science and knowledge-based applications. Proposal submission We welcome workshop and tutorial proposals that are of relevance to the topics listed below. Submissions should be consistent with the main conference formatting guidelines and be 3–5 pages in length, plus unlimited pages for appendices. The decision on acceptance or rejection of a workshop proposal will be made on the basis of the overall quality of the proposal and its appeal to linguistics and knowledge-based communities. Other factors, such as overlap with other workshop proposals, or issues regarding logistics, will also be taken into account when making the final decision. Submissions should include the following details: 1. Title of the workshop or tutorial 2. List of organisers or tutorial presenters: List the names, affiliations, home page links, and provide short (one paragraph) biographies for all workshop organisers or tutorial presenters. 3. List of topics: For workshops, provide a list of topics of relevance to the workshop. For tutorials, provide a detailed outline of the topics that will be covered. 4. Detailed description: Provide a 200-word summary that motivates and describes the list of topics in further detail. 5. Past workshops or tutorials: Describe any previous editions of the same workshop or tutorial, along with an estimated size of the audience. 6. Format: Describe the proposed format for the event (physical/virtual). For instance, for workshops, describe the envisioned mix, i.e. invited talks, submitted paper presentations, poster sessions, panels, or demo sessions. For tutorials, describe to what extent the tutorial will consist of slide presentations vs. practical hands-on training. 7. Expected audience: Describe the expected size and composition of the audience. 8. Duration: Indicate if the workshop or tutorial is planned to last half a day (4h) or a full day (8h). 9. Technical requirements: List any special requirements, such as audio-visual equipment, poster boards, etc. 10. Committee members: For workshops, if available, provide a preliminary list of people who have agreed to serve as program committee members. 11. References: List relevant publications. Accepted workshops will be required to prepare a workshop website containing their call for papers and detailed information about the workshop organisation and timelines. Language: All submissions must be written in English. Proposals should be submitted via EasyChair at: https://easychair.org/cfp/LDK2023WP. Each proposal will be reviewed by the workshop and tutorial chairs and relevant members of the organising committee of LDK2023, and ranked based on the overall quality of the proposal and the workshop’s fit to the conference as detailed below. In particular, workshops should address research topics that satisfy each of the following criteria: 1. The topic proposed by the workshops should fall in the general scope of LDK2023. 2. The workshop’s target should be clear and anchor a specific technology, problem or application. 3. The aim of the workshop is to attract a broad community of interested individuals. 4. The format of the workshop can invite various types of contributions (oral and poster presentations) and can be accommodated in online/hybrid settings. 5. Workshop proposals on emerging topics are encouraged. Topics: 1. Fundamental technical and theoretical problems of language data / linked data and knowledge graphs. 2. Applications of language data and semantic web technologies in domains such as law, medicine, life science, digital humanities, mobility and smart cities, etc. 3. Research areas that have been largely neglected or underrepresented in language data and semantic web studies. 4. Other research areas relevant to language data and semantic web research (such as data science, artificial intelligence, big data analytics, human-computer interaction, natural language processing, and information retrieval). 5. New emerging topics. We encourage diversity in the organising and program committee: from different institutions and the presence of young researchers and PhD students. At least one of the organisers should be registered to LDK2023. All organisers (presenters) of accepted workshops are expected to: 1. Have their own (open) reviewing process and take care of their publicity (e.g., website, timelines, and call for papers). 2. Workshop organisers will be asked to: 1. Attend LDK2023 in person (at least one organiser). 2. Closely cooperate with the Workshop Chairs to finalise all organisational details. Workshop and tutorial chairs: - Ana Ostroški Anić – Institute of Croatian Language and Linguistics - Blerina Spahiu – University of Milano-Bicocca Important Dates: 19 Dec 22 Proposal submission deadline 16 Jan 23 Notification of accepted proposals 6 Feb 2023 Workshop website and CfP published 19 May 23 Suggested deadline for workshop paper submissions 16 June 23 Suggested deadline for notification for workshop paper submissions 30 June 23 Camera-ready submission deadline 13 September 2023 Workshop and tutorial day 14–15 September 2023 Main conference All deadlines refer to anywhere-on-earth time. Ana Ostroški Anić and Blerina Spahiu (Workshop and Tutorial Chairs)

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Call for Papers: The First Workshop on Context-aware NLP in eHealth, ICON 2022, India
by Mohammed Hasanuzzaman 29 Oct '22

29 Oct '22

Dear List members, WNLPe-Health 2022 - the first Workshop on Context-aware NLP in eHealth will be held at IIIT Delhi, India on December 15th, 2022 in conjunction with 19th International Conference on Natural Language Processing (ICON 2022) . It is currently recognised that as much as 30% of the world’s stored data is produced by the healthcare sector. However, this ‘data-rich’ sector does not currently explore data to the full potential which may allow the development of much more individual and person-centred AI technologies. For example, by combining ubiquitous data with user-generated and publicly available data, AI algorithms can guide and inform citizens about risk modifying behaviors in an appropriate context. Context can be defined as “any information that can be used to characterize the situation of an entity. An entity is a person, place, or object that is considered relevant for the interaction between a user and an application, including the user and applications themselves.” The goal of this workshop is to provide a unique platform to bring together researchers and practitioners in healthcare informatics working with health-related data, especially textual data, and facilitate close interaction among students, scholars, and industry professionals on eHealth language processing tasks. In particular, we are interested in works that advance state-of-the-art NLP and ML techniques for eHealth domains by incorporating more contextual knowledge in order to make models explainable, trustable and robust in changing situations. We are interested in research on novel approaches, works in progress, comparative analyses of tools, and advancing state-of-the-art work in eHealth NLP methods, tools, and applications. Relevant topics for the workshop include, but are not limited to, the following areas: - Modelling of healthcare text in classical NLP tasks (tagging, chunking, parsing, entity identification, relation extraction, coreference, summarization, etc.) for under-resourced languages. - Person-centred NLP applications for eHealth including early risk prediction. - Algorithm for Context Data reasoning. - Context sensitive recommendations to individual citizens and patients. - Integration of structured and unstructured resources for health applications. - Domain adaptation techniques for clinical data. - Medical terminologies and ontologies. - Interpretability and analysis of NLP models for healthcare applications. - Processing clinical literature and trial reports. - Bayesian modelling and feature selection techniques for high-dimensional healthcare data. - Multimodal learning for decision support systems: Ubiquitous data, public databases, user generated content (in combination with wearable sensor technology). Full paper submissions are limited to 8 pages, while short paper submissions should be less than 4 pages (including bibliography). For more information: https://sites.google.com/view/wnlpe-health2022/submissions Important Dates Paper submission deadline: 20th November, 2022 (23:59 Hawaii Standard Time) Notification of acceptance: 26th November, 2022 Camera ready copy deadline: 1st December, 2022 Workshop date: 15th December, 2022 Best regards, Mohammed <https://sites.google.com/view/wnlpe-health2022/submissions#h.jxoic3mpxg99> Important Dates Paper submission deadline: 20th November, 2022 (23:59 Hawaii Standard Time) Notification of acceptance: 26th November, 2022 Camera ready copy deadline: 1st December, 2022 Workshop date: 15th December, 2022 ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ *Dr. Mohammed Hasanuzzaman, Lecturer, Munster Technological University <https://www.mtu.ie/> * *Funded Investigator, ADAPT Centre- <https://www.adaptcentre.ie/> A <https://www.adaptcentre.ie/>* World-Leading SFI Research Centre <https://www.adaptcentre.ie/> *Member, Lero, the SFI Research Centre for Software <https://lero.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* Dept. of CS Munster Technological University Bishopstown campus Cork e: mohammed.hasanuzzaman(a)adaptcentre.ie <email(a)adaptcentre.ie>/ Ireland 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…> 29/10/22, 02:19:47

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Internship at GETALP-LIG-UGA - 5/6 months : Context-Aware Neural Machine Translation Evaluation
by Emmanuelle Esperança-Rodier 28 Oct '22

28 Oct '22

Dear colleagues, Please find hereafter an internship proposal. Feel free to transfer it to your M2 students. Kind regards ---------------------------------------------------------------------------------------------------------------------------------- The LIG (Laboratoire d'Informatique de Grenoble) proposes the following Master 2 level internship: Title: Context-Aware Neural Machine Translation Evaluation Description: Context-Aware Neural Machine Translation (CA-NMT) [Tiedemann and Scherrer, 2017; Laubli et al., 2018; Miculicich et al., 2018; Maruf et al., 2019; Zheng et al., 2020; Ma et al. 2021; Lupo et al., 2022] is currently one of the main research axes in NLP, with strong impact on both academic and company research. CA-NMT systems are evaluated with both "average-quality-measuring" metrics such as BLEU [Papineni et al., 2002], and dedicated contrastive test suites [Voita et al., 2019; Muller&Rios 2018; Lopes et al., 2020]. The latter have been designed to measure specifically to which degree CA-NMT systems are able to exploit context while scoring sentences to be translated in context. Indeed the average translation quality measured by BLEU has been shown inadequate in this respect [Lupo et al., 2022]. When evaluating models with contrastive test suites however, models are only asked to score sentences and not to translate them. The ability of models to use context is thus only implicitly evaluated. With the work planned in this internship we would like to make a step ahead in the evaluation of CA-NMT systems. The idea is to exploit annotated data like those already used for [Muller&Rios 2018; Lopes et al., 2020] to explicitly involve discourse phenomena, such like coreferences and anaphora, in the evaluation procedure of CA-NMT models. Such evaluation procedure will allow possibly to design more accurate and adequate evaluation measures for "discourse-phenomena-aware" CA-NMT systems. Practical Aspects: In this internship the student will use Machine Learning and Deep Learning tools to automatically annotate parallel data (at least English-French, but possibly also English-German and other language pairs) used for NMT with discourse phenomena, as well as Neural Machine Translation tools for automatically generating translations that will be used for CA-NMT evaluation. Based on the annotation of discourse phenomena, we will design an adequate evaluation metric for CA-NMT systems, taking into account the capability of the system to exploit discourse phenomena. Finally, the evaluation metric will be tested by evaluating CA-NMT systems already available or trained from scratch at LIG by the student. Profile: Master 2 student level in computer science or NLP Interested in Natural Language Processing and Deep Learning approaches Skills in machine learning for probabilistic models Computer science skills: Python programming. Some knowledge of deep learning libraries such like Pytorch (Fairseq would be a plus). Data manipulation and annotation The internship may last from 5 up to 6 months, it will take place at LIG laboratory, GETALP team (http://lig-getalp.imag.fr/ <http://lig-getalp.imag.fr/>), starting from January/February 2022. The student will be tutored by Marco Dinarelli (http://www.marcodinarelli.it <http://www.marcodinarelli.it/>), andEmmanuelle Esperança-Rodier (https://lig-membres.imag.fr/esperane/ <https://lig-membres.imag.fr/esperane/>) Interested candidates must send a CV and a motivation letter to (both adresses) marco.dinarelli(a)univ-grenoble-alpes.fr <mailto:marco.dinarelli@univ-grenoble-alpes.fr>, Emmanuelle.Esperanca-Rodier(a)univ-grenoble-alpes.fr <mailto:Emmanuelle.Esperanca-Rodier@univ-grenoble-alpes.fr>. [Tiedemann and Scherrer, 2017] Neural ma- chine translation with extended context. Workshop on Discourse in Machine Translation 2017. [Laubli et al., 2018] Has machine translation achieved human parity? a case for document-level evaluation. EMNLP 2018. [Miculicich et al. 2018] Document-level neural machine translation with hierarchical attention networks. EMNLP 2018. [Maruf et al., 2019] Selective attention for context-aware neural machine translation. NAACL 2019. [Zheng et al., 2020] Towards Making the Most of Context in Neural Machine Translation. IJCAI 2020. [Ma et al., 2021] A Comparison of Approaches to Document-level Machine Translation. arXiv pre-print 2021. [Lupo et al., 2022] Divide and Rule: Effective Pre-Training for Context-Aware Multi-Encoder Translation Models. ACL 2022. [Papineni et al., 2022] Bleu: a method for automatic eval- uation of machine translation. ACL 2002. [Voita et al., 2019] "When a good translation is wrong in context: Context-aware machine translation improves on deixis, ellipsis, and lexical cohesion". ACL 2019. [Muller&Rios 2018] "A large-scale test set for the evaluation of context-aware pronoun translation in neural machine translation." CMT 2018 [Lopes et al., 2020] "Document-level neural MT: A systematic comparison". EAMT 2020 ---------------------------------------------------------------------------------------------------------------------------------- ___________________________________________ Emmanuelle Esperança-Rodier Enseignante-Chercheuse en Linguistique Informatique (Section 7) Maîtresse de Conférences - Hors Classe UMR 5217 - LIG (Laboratoire d’Informatique de Grenoble) GETALP (Groupe d’Étude en Traduction Automatique/Traitement Automatisé des Langues et de la Parole) Bâtiment IMAG - 700 avenue Centrale - Domaine Universitaire de Saint-Martin-d’Hères 04 57 42 14 92 Service des Langues UGA Coordinatrice des enseignements d’anglais pour la composante IM2AG - Mathématiques

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Internship offer (with PhD opportunity), LS2N, Nantes, France. Diving into neural language models for improving discourse analysis tasks
by Nicolas HERNANDEZ 28 Oct '22

28 Oct '22

* Title: Diving into neural language models for improving discourse analysis tasks * Keywords: Neural Language Models, Discourse analysis, Argumentative structure, Probing, Transfer Learning * Supervisors: Nicolas.Hernandez(a)univ-nantes.fr and Laura.Monceaux(a)univ-nantes.fr * Location: TALN@LS2N, Nantes, France - https://taln-ls2n.github.io * Starting date: Jan-2023 (flexible) ~6 months * Opportunity: to pursue a PhD in the Lexhnology ANR project https://www.ls2n.fr/stage-these/diving-into-neural-language-models-for-impr… # MISSION Fine-tuning a pre-trained language model has become the de facto standard for handling natural language processing tasks. Since many of these tasks are dealing with discourse and dialogue structures (e.g. conversational agent, summarization, dialogue acts recognition, argumentation mining), it is crucial to understand how such information is captured by the language models and to study how to intervene on the learning of this type of information: what is learned, what is missing, how to add it, how to keep the useful information in a fine-tuned, distilled, pruned or quantized model... The internship mission will be defined in this context, collaboratively with the candidate. One possibility would be to start by probing the language models on discourse analysis tasks. We wish the successful candidate to pursue a PhD on the subject in the Lexhnology project. * A. Rogers, O. Kovaleva, and A. Rumshisky. A Primer in BERTology: What We Know About How BERT Works. Transactions of the Association for Computational Linguistics (TACL), 8:842–866. 2020. * V. Araujo, A. Villa, M. Mendoza, M.-F. Moens, and A. Soto, “Augmenting BERT-style Models with Predictive Coding to Improve Discourse-level Representations,” In EMNLP, Nov. 2021. * M. Lukasik, B. Dadachev, G. Simões, & K. Papineni, Text Segmentation by Cross Segment Attention, In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 4707–4716, November 16–20, 2020. * L. Huber, C. Memmadi, M. Dargnat, and Y. Toussaint. Do sentence embeddings capture discourse properties of sentences from scientific abstracts ? In the First ACL Workshop on Computational Approaches to Discourse, 86–95, 2020. * F. Koto, J. H. Lau, and T. Baldwin. Discourse Probing of Pretrained Language Models. In Proceedings of the 20th Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), Mexico (virtual), 2021 # THE LEXHNOLOGY PROJECT Lexhnology is a project funded by the French National Agency (ANR). It will start on January 2, 2023 for a period of 42 months. Given the growing extraterritoriality of American law, this domestic law is increasingly impacting other countries' jurisdiction. It is of prime importance that second-language (L2) users of legal English be able to analyze case law. Teaching the argumentative structure to L2 learners is a widely accepted method in languages for specific purposes (LSP) L2 teaching/learning and may help learners understand the legally-binding rationale behind judicial decisions. Despite this context, consensus about the linguistic definition of the communicative functions, also known as moves, in case law does not yet exist. In addition, no Natural Language Processing (NLP) techniques are currently able to automatically identify moves in case law. Finally, the effectiveness of making moves explicit to L2 learners has not been measured experimentally. To answer these questions, Lexhnology will take an innovative interdisciplinary approach – linguistic, NLP, LSP teaching/learning. The project is the joint collaboration of four laboratories, namely LS2N, CRINI, LAIRDIL and ATILF. # APPLICATION The successful candidate is expected to: * Have/Prepare a Master Degree (or equivalent) in Natural Language Processing, Computer Sciences, Computational Linguistics or Data sciences, * Have a excellent background in deep learning and more generally machine learning, * Have strong programming skills (software dev. and python) * Have good verbal communication and writing skills (in French/English) * Have facility with teamwork as well as working autonomously * Be dynamic and curious We look forward to receiving your meaningful online application including: * a letter of motivation * a CV * contacts for two references

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[Jobs] ELLIS PhD Program: 150+ PhD positions in machine learning (incl. NLP)
by lynn.anthonissen＠uni-tuebingen.de 28 Oct '22

28 Oct '22

Apply by November 15, 2022, to join the ELLIS PhD Program in 2023 – Details at: https://ellis.eu/news/ellis-phd-program-call-for-applications-2022 The ELLIS PhD program has launched its yearly recruiting round and is now accepting applications. A key pillar of the ELLIS initiative, the program's central aim is to foster and educate the best talent in machine learning and related research areas by pairing outstanding students from across the globe with leading researchers in Europe. The program also offers a variety of networking and training activities. Each PhD student is co-supervised by two ELLIS scientists based in different European countries. Over the course of their degree, students complete a mandatory exchange of at least six months at their co-advisor's lab. One of the advisors may also come from industry, in which case the student will collaborate closely with the industry partner and spend their exchange conducting research at an industrial lab. Research areas include (but are not limited to) the following machine learning-driven research fields: - AutoML - Bayesian & Probabilistic Learning - Bioinformatics - Causality - Computational Neuroscience - Computer Graphics - Computer Vision - Deep Learning - Earth & Climate Sciences - Health - Human Behavior, Psychology & Emotion - Human Computer Interaction - Human Robot Interaction - Information Retrieval - Interactive & Online Learning - Interpretability & Fairness - Law & Ethics - Machine Learning Algorithms - Machine Learning Theory - ML in Chemistry & Material Sciences - ML in Finance - ML in Science & Engineering - ML Systems - Multi-agent Systems & Game Theory - Natural Language Processing - Optimization & Meta Learning - Privacy - Quantum & Physics-based ML - Reinforcement Learning & Control - Robotics - Robust & Trustworthy ML - Safety - Security, Synthesis & Verification - Symbolic Machine Learning - Unsupervised Learning You can watch our introductory video here: https://www.youtube.com/watch?v=kWXNpnxkfg0. The deadline for applications is November 15, 2022. Interested candidates should apply online through the ELLIS application portal. For details on the program, specific research areas, and the application process, please consult the call for applications: https://ellis.eu/news/ellis-phd-program-call-for-applications-2022.

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PhD studentship in Natural Language Processing at the University of Edinburgh funded by DeepMind
by Mirella Lapata 28 Oct '22

28 Oct '22

The School of Informatics, University of Edinburgh, is thrilled to announce a PhD scholarship funded by DeepMind. The scholarship covers tuition fees (at the Home/International tuition fee rate), provides annual stipend of £17,668 annum (for 4 years full time study) and provides a research training and support grant. The student will be supervised by Dr. Mirella Lapata and will also benefit from mentoring from DeepMind staff during their period of study.  Applicants would be expected to work on an topic drawn from the following research areas: - multimodal natural language understanding and generation - long-form and retrieval-augmented text generation - Multilingual generation Applicants wishing to apply for the scholarship should meet one OR both of the following criteria: - are resident of a country and/or region underrepresented in AI; - identify as women including cis and trans people and non-binary or gender fluid people who identify in a significant way as women or female; - and/or identify as Black or other minority ethnicity; The successful candidate will have a good honours degree or equivalent in artificial intelligence, computer science, machine learning, or a related discipline; or have a breadth of relevant experience in industry/academia/public sector, etc. They will have strong programming skills and previous experience in natural language processing. If you have further questions, please Dr. Mirella Lapata, mlap(a)inf.ed.ac.uk. To apply, please follow the instructions at: http://www.inf.ed.ac.uk/postgraduate/apply.html As your research area, please select "Informatics: ILCC: Language Processing, Speech Technology, Information Retrieval, Cognition". On the application form under "Research Project", please state "DeepMind Scholarship". IMPORTANT: After submitting your application through the website, please email your applicant number to mlap(a)inf.ed.ac.uk. Application deadline: 10th December 2022 [applications received after the deadline may be considered, but this cannot be guaranteed]. -- The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336.

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Webminar by Vered Shwartz (The University of British Columbia-Vancouver)
by HiTZ Zentroa 28 Oct '22

28 Oct '22

Dear colleague, We are happy to announce the next webinar in the Language Technology webinar series organized by the HiTZ research center (Basque Center for Language Technology, http://hitz.eus). We are organizing one seminar every month. You can check the videos of previous webinars and the schedule for upcoming webinars here: http://www.hitz.eus/webinars Next webinar: * *Speaker*: Vered Shwartz (The University of British Columbia-Vancouver) * *Title*: Incorporating Commonsense Reasoning into NLP Models * *Date*: November 3, 2022, 15:30 CET * *Summary*: NLP models are primarily supervised, and are by design trained on a sample of the situations they may encounter in practice. The ability of models to generalize to and address unknown situations reasonably is limited, but may be improved by endowing models with commonsense knowledge and reasoning skills. In this talk, I will present several lines of work in which commonsense is used for improving the performance of NLP tasks: for completing missing knowledge in underspecified language, interpreting figurative language, and resolving context-sensitive event coreference. Finally, I will discuss open problems and future directions in building NLP models with commonsense reasoning abilities. * *Bio*:Vered Shwartz is an Assistant Professor of Computer Science at the University of British Columbia and a faculty member at the Vector Institute for Artificial Intelligence. Her research interests include commonsense reasoning, computational semantics and pragmatics, and multiword expressions. Previously, Vered was a postdoctoral researcher at the Allen Institute for AI (AI2) and the University of Washington, and received her PhD in Computer Science from Bar-Ilan University. * *Upcoming webinars*: o Machine translation as a tool for multilingual information: different users and use scenarios -- Maarit Koponen (December 1, 2022) Check past and upcoming webinars at the following url: http://www.hitz.eus/webinars If you are interested in participating, please complete this registration form: http://www.hitz.eus/webinar_izenematea If you cannot attend this seminar, but you want to be informed of the following HiTZ webinars, please complete this registration form instead: http://www.hitz.eus/webinar_info Best wishes, HiTZ Zentroa

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