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[Corpora-List]TRAC 2022: Second CFP and Shared Task Participation (COLING 2022 Workshop)
by Ritesh Kumar 24 Jun '22

24 Jun '22

*For this iteration of the shared task, we especially encourage those who participated or have trained models on TRAC - 2018 and /or TRAC - 2020 Shared Task datasets to submit the predictions of their earlier models on our current test set. They are, of course, free to submit predictions on new models / current datasets as well.* *3rd Workshop on Threat, Aggression and Cyberbullying (TRAC - 2022)* > > & > *Shared Tasks on Bias, Threat and Aggression Identification in Context* > Co-located with COLING 2022, October 12 - 17, 2022 > Gyeongju, the Republic of Korea > > > *Second Call for Papers and Shared Task Participation* > > *Workshop Website*: https://sites.google.com/view/trac2022/home > *Paper Submission*: https://www.softconf.com/coling2022/TRAC-2022/ > *Shared Task Website:* https://codalab.lisn.upsaclay.fr/competitions/4753 > > *Submission Deadline*: July 11, 2022 (Regular) / July 31, 2022 (ACL ARR) > > As in the earlier editions of the workshop, TRAC-2022 will focus on the > applications of NLP, ML and pragmatic studies on aggression and > impoliteness to tackle these issues. We invite *long (8 pages)* and *short > papers (4 pages)* as well as *position papers* and opinion pieces (5 - 20 > pages), *demo proposals* and *non-archival extended abstracts* (2 pages) > based on, but not limited to, any of the following themes from academic > researchers, industry and any other group / team working in the area. > > - Theories and models of aggression and conflict in language. > - Cyberbullying, threatening, hateful, aggressive and abusive language > on the web. > - Multilingualism and aggression. > - Resource Development - Corpora, Annotation Guidelines and Best > Practices for threat and aggression detection. > - Computational Models and Methods for aggression, hate speech and > offensive language detection in text and speech. > - Detection of threats and bullying on the web. > - Automatic censorship and moderation: ethical, legal and > technological issues and challenges. > > > *Shared Tasks* > TRAC-2022 will include two novel shared tasks: > > *Task 1: Bias, Threat and Aggression Identification in Context* > The first shared task will be a structured prediction task for recognising > (a) Aggression, Gender Bias, Racial Bias, Religious Intolerance and Bias > and Casteist Bias on social media and (b) the "discursive role" of a given > comment in the context of the previous comment(s). The participants will be > given a "thread" of comments with information about the presence of > different kinds of biases and threats (viz. gender bias, gendered threat > and none, etc) and its discursive relationship to the previous comment as > well as the original post (viz. attack, abet, defend, counter-speech and > gaslighting). In a series / thread of comments, participants will be > required to predict the presence of aggression and bias of each comment, > possibly making use of the context. > > *Task 2: Generalising across domains - COVID-19* > For this sub-task, the test set will be sampled from the COVID-19 related > conversation, annotated with levels of aggression, offensiveness and hate > speech. Across the globe, during the pandemic, we have seen various kinds > of novel aggressive and biased conversation on social media - in fact, in > some cases there was massive escalation of religious and other kinds of > intolerance and polarisation. The participants of TRAC-1 and TRAC-2 shared > tasks are especially encouraged to submit the predictions their their > earlier models on this test set. They may also train new models jointly on > both the datasets. Those who didn't participate in earlier tasks are also > invited to submit the predictions for this task by training models on the > two datasets and are encouraged to submit the predictions on the respective > test sets of the earlier tasks along with the predictions on the current > dataset (to enable comparison). New participants may also use TRAC-1 or > TRAC-2 dataset or a combination of the two for building the models. The aim > of the task is to evaluate the generalisability of our systems in > unexpected and novel situations. > > For participation, visit the Codalab website - > https://codalab.lisn.upsaclay.fr/competitions/4753 > > For any clarifications, contact coling.aggression(a)gmail.com. > > Looking forward to your participation! > >

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[Corpora-List]Multiple NLP Postdoc Positions available at the CSIRO - Deadline: 7 July 2022
by Wan, Stephen (Data61, Eveleigh) 24 Jun '22

24 Jun '22

Multiple CSIRO Early Research Career Postdoctoral Fellowships are available in Natural Language Processing. CSIRO Data61 is looking for multiple CERC Fellows to join an NLP team of researchers and engineers. Relevant NLP research areas: information extraction, text summarization, question answering, semantic parsing, semantic role labelling, paraphrase detection and generation, and NLP for Information Retrieval. About the CSIRO Postdoctoral Fellowship program: CSIRO Early Research Career (CERC) Postdoctoral Fellowships provide opportunities to scientists and engineers who have completed their doctorate and have less than three years of relevant postdoctoral work experience. These fellowships aim to develop the next generation of future leaders of the innovation system. Location: Sydney, NSW Salary: AU$89k - AU$98k plus up to 15.4% superannuation Tenure: Specified term of 3 years Reference: 77986 Applications close: 7 July 2022 To be considered you will need: * A doctorate (or will shortly satisfy the requirements of a PhD) in a relevant discipline area, such as Computer Science (Natural Language Processing/Computational Linguistics or Machine Learning with text data). * Experience using deep learning and other machine learning techniques in NLP. * High-level written and oral communication skills with the ability to represent the research team effectively internally and externally, including the presentation of research outcomes at national and international conferences. * A sound history of publication in peer-reviewed journals and/or conferences. For more information or to apply, please visit: https://jobs.csiro.au/job-invite/77986/

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[Corpora-List]Complex Word Identification in French
by Christopher Collins 23 Jun '22

23 Jun '22

Hello, I'm looking for any open source or cloud-hosted solution for complex word identification or word difficulty rating in French for a reading application. As a backup plan we can use measures like corpus frequency, length, number of senses, but we're hoping someone has already made a tool available. We found this but that's it: https://github.com/sheffieldnlp/cwi Would appreciate any tips! Thanks, Chris Christopher Collins [he/him<https://medium.com/gender-inclusivit/why-i-put-pronouns-on-my-email-signatu…>] Associate Professor - Faculty of Science Canada Research Chair in Linguistic Information Visualization Ontario Tech University vialab.ca<http://vialab.ca/>

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[Corpora-List]Call for Participation: WAT2022 Multi-Modal Translation Task
by Shantipriya Parida 23 Jun '22

23 Jun '22

[Apologies for cross-posting] Dear All, We invite you to participate in three shared tasks. The aim of the “WAT 2022 Multi-Modal translation task” is to enable research on multilingual multimodal NLP. Workshop on Asian Translation (WAT) is organizing the multimodal English-to-Hindi, English-to-Malayalam, and English-to-Bengali translation tasks using the multimodal datasets, consisting of text and images suitable for English-to-Hindi, English-to-Malayalam, and English-to-Bengali multimodal machine translation, Image Captioning task and multimodal research. Timeline: Please refer to the task website. Websites: https://ufal.mff.cuni.cz/hindi-visual-genome/wat-2022-english-hindi-multimod https://ufal.mff.cuni.cz/malayalam-visual-genome/wat2022-english-malayalam-… https://ufal.mff.cuni.cz/bengali-visual-genome/wat-2022-english-bengali-mul… Contact: email: wat-multimodal-task(a)ufal.mff.cuni.cz (same contact for all tasks) Organizers: Shantipriya Parida <https://www.linkedin.com/in/shantipriya-parida-9781a9127/>, Silo AI, Helsinki, Finland Ondřej Bojar, <https://ufal.mff.cuni.cz/ondrej-bojar> Charles University, Prague, Czech Republic -- Shantipriya Parida Senior AI Scientist @ Silo AI <http://www.silo.ai/> Mobile:# +358 (0465787840) LinkedIn <http://linkedin.com/>*Shantipriya Parida <https://www.linkedin.com/in/shantipriya-parida-9781a9127/>* *http://www.shantipriya.me/ <http://www.shantipriya.me/>*

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[Corpora-List]NeuroX: Neuron Interpretation Toolkit
by Hassan Sajjad 23 Jun '22

23 Jun '22

Dear colleagues, We have developed a toolkit to interpret deep NLP models with a focus on *neuron interpretation*. The toolkit is available as: pip install neurox Project Website: https://neurox.qcri.org/ Documentation: https://neurox.qcri.org/docs/ Git: https://github.com/fdalvi/NeuroX NeuroX implements a number of features that facilitate model interpretation such as: - Word-level activation extractions (contextualized embeddings) - Integration with Huggingface - Implementation of a number of neuron probing methods (identify neurons learning a linguistic property such as noun) - Visualize the behavior of a neuron across a set of examples - and many more If you have any questions or feedback, feel free to reach out to me or open an issue on the project's github. Thank you, -- Regards; Hassan Sajjad

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[Corpora-List]SOA models for Sentiment Analysis benchmarked on Multiple Domains ( Model and Paper )
by Giuseppe Riccardi 23 Jun '22

23 Jun '22

Dear all, we are glad to present to you a new BERT-based model for Sentiment Analysis ( for Italian ), trained and benchmarked on multiple domains! The model has been jointly optimized and fine-tuned on multiple domains such as product reviews, social media comments and financial news. The model has achieved better performance than fine-tuning it in isolation on every single dataset, reaching state-of-the-art results in the majority of the datasets that we used. To get and use the model please, follow the instructions available here: https://sisl.disi.unitn.it/itfn-corpus/ <https://sisl.disi.unitn.it/itfn-corpus/> or you can go directly to the official GitLab repo: https://gitlab.com/sislab/multi-source-multi-domain-sentiment-analysis-with… <https://gitlab.com/sislab/multi-source-multi-domain-sentiment-analysis-with…> The related paper will be presented at LREC 2022 conference. The paper is titled as "Multi-source Multi-domain Sentiment Analysis with BERT-based Models" and it is available here <http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.62.pdf>. Best Regards ---- Prof. Dr.-Ing. Giuseppe Riccardi Founder and Director of the Signals and Interactive Systems Lab Department of the Department of Computer Science and Engineering Department University of Trento

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[Corpora-List]16 positions in AI and ML at the Finnish center for AI
by Tiedemann, Jörg 22 Jun '22

22 Jun '22

FCAI is hiring! 16 funded positions in artificial intelligence & machine learning for postdocs, research fellows, PhD students. Apply now: fcai.fi/we-are-hiring For NLP folks: look at section 2 and uncertainty-aware language and speech understanding!

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[Corpora-List]CFP- SocialDisNER track (SMM4H/COLING) : Detection of Disease Mentions in Social Media.
by Martin Krallinger 22 Jun '22

22 Jun '22

CFP- SocialDisNER track: Detection of Disease Mentions in Social Media (SMM4H Shared Task at COLING2022) https://temu.bsc.es/socialdisner/ <https://mailtrack.io/trace/link/6766192d9893dae46724eb77b400118fb7bc69da?ur…> Despite the high impact & practical relevance of detecting diseases automatically from social media for a diversity of applications, few manually annotated corpora generated by healthcare practitioners to train/evaluate advanced entity recognition tools are currently available. Developing disease recognition tools for social media is critical for: - Real-time disease outbreak surveillance/monitoring - Characterization of patient-reported symptoms - Post-market drug safety - Epidemiology and population health, - Public opinion mining & sentiment analysis of diseases - Detection of hate speech/exclusion of sick people - Prevalence of work-associated diseases SocialDisNER is the first track focusing on the detection of disease mentions in tweets written in Spanish, with clear adaptation potential not only to English but also other romance languages like Portuguese, French or Italian spoken by over 900 million people worldwide. For this track the SocialDisNER corpus was generated, a manual collection of tweets enriched for first-hand experiences by patients and their relatives as well as content generated by patient-associations (national, regional, local) as well as healthcare institutions covering all main diseases types including cancer, mental health, chronic and rare diseases among others. Info: - Web: https://temu.bsc.es/socialdisner/ <https://mailtrack.io/trace/link/45af6f43eba33e6ef6747ff1b0de9ef5057f18a8?ur…> - Data: <https://mailtrack.io/trace/link/d9d74054359cab6ad746e882275ddaac19f9a419?ur…> https://doi.org/10.5281/zenodo.6359365 <https://mailtrack.io/trace/link/d5a25ef767660e4d57fc35463a5f83939fa875ce?ur…> - Registration: https://temu.bsc.es/socialdisner/registration <https://mailtrack.io/trace/link/94516241effc9203151687423b20243e01903c77?ur…> Schedule - Development Set Release: June 14th - Test Set Release: July 11th - Participant prediction Due: July 15th - Test set evaluation release: July 25th - Proceedings paper submission: August 1st - Camera ready papers: September 1st - SMM4H workshop @ COLING 2022: October 12-17 Publications and SMM4H (COLING 2022) workshop Participating teams have the opportunity to submit a short system description paper for the SMM4H proceedings (7th SMM4H Workshop, co-located at COLING 2022). More details are available at https://healthlanguageprocessing.org/smm4h-2022/ <https://mailtrack.io/trace/link/0312fb47c8ed5e3bcb7fabd23fc7fcee6ed3e008?ur…> SocialDisNER Organizers - Luis Gascó, Barcelona Supercomputing Center, Spain - Darryl Estrada, Barcelona Supercomputing Center, Spain - Eulàlia Farré-Maduell, Barcelona Supercomputing Center, Spain - Salvador Lima, Barcelona Supercomputing Center, Spain - Martin Krallinger, Barcelona Supercomputing Center, Spain Scientific Committee & SMM4H Organizers - Graciela Gonzalez-Hernandez, Cedars-Sinai Medical Center, USA - Davy Weissenbacher, University of Pennsylvania, USA - Arjun Magge, University of Pennsylvania, USA - Ari Z. Klein, University of Pennsylvania, USA - Ivan Flores, University of Pennsylvania, USA - Karen O’Connor, University of Pennsylvania, USA - Raul Rodriguez-Esteban, Roche Pharmaceuticals, Switzerland - Lucia Schmidt, Roche Pharmaceuticals, Switzerland - Juan M. Banda, Georgia State University, USA - Abeed Sarker, Emory University, USA - Yuting Guo, Emory University, USA - Yao Ge, Emory University, USA - Elena Tutubalina, Insilico Medicine, Hong Kong - Jey Han Hau, The University of Melbourne (Australia) - Luca Maria Aiello, IT University of Copenhagen - Rafael Valencia-Garcia, Universidad de Murcia (Spain) - Antonio Jimeno Yepes, RMIT University (Australia) - Eugenio Martinez Cámara, Universidad de Granada (Spain) - Gema Bello Orgaz, Applied Intelligence and Data Analysis Research Group, Universidad Politécnica de Madrid (Spain) - Héctor D. Menendez, King's College London (UK) - Manuel Montes y Gómez, National Institute of Astrophysics, Optics and Electronics (Mexico) - Helena Gómez Adorno, Universidad Nacional Autónoma de México (Mexico) - Rodrigo Agerri, IXA Group (HiTZ Centre), University of Basque Country EHU (Spain) - Miguel A. Alonso, Universidad da Coruña (Spain) - Ferran Pla, Universidad Politécnica de Valencia (Spain) - Jose Alberto Benitez-Andrades, Universidad de Leon (Spain)

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[Corpora-List]CfP Special issue "Special Issue "Machine Translation: Principles and Challenges"
by Natalia Resende 22 Jun '22

22 Jun '22

We are inviting contributions to a special issue of the journal *Information* (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence". *Deadline for manuscript submissions: 15 November 2022 | Viewed by 122* *More information:* https://www.mdpi.com/journal/information/special_issues/machine_translation… *Special Issue Editors* Dr. Natalia Resende Guest Editor School of Computer Sciences, CeADAR, University College Dublin, Dublin, Ireland Contact: natalia.resende(a)ucd.ie Dr. Pamela Toassi E-Mail Website Guest Editor Department of English Studies, its Literature and Translation (DELILT/UFC), Postgraduate Program in Linguistics (PPGLin/UFC), Postgraduate Program in Translation (POET/UFC), Federal University of Ceara (UFC), Fortaleza, CE, Brazil Dear Colleagues, Due to advances in the field of artificial intelligence that have led to significant improvements in the performance of machine translation (MT) systems, as well as the availability of popular tools that can be accessed free of cost, the number of MT users has increased significantly over the last few years. The evolution of this technology has also resulted in its incorporation in the translation industry as MT represents a powerful tool for increasing translation productivity. However, in the comparison of machine-translated texts and human-translated texts, several questions remain unanswered, especially those concerning quality assessment in MT. For this Special Issue, we are inviting original and unpublished works addressing issues in machine translation that arise in the search for human parity. We are especially interested in studies focusing on MT translation quality and post-editing; innovative solutions and techniques concerning MT performance; studies involving new approaches for systems‘ implementation; techniques for MT evaluation; examination of linguistic phenomena in the output of MT systems; corpora for MT; the impact of MT in society; and MT user experience. We will also accept works committed to advancing theoretical contributions in the field. Topics of interest include but are not limited to: Machine translation evaluation; Post-editing of machine translation ; Resources for machine translation; Machine translation‘s impact on society; MT user experience; New approaches to machine translation‘s implementation and deployment; Incorporation of linguistic knowledge in machine translation; Theoretical contributions to the field of machine translation. Dr. Natalia Resende Dr. Pamela Toassi Guest Editors

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[Corpora-List]We offer two fully funded PhD positions at IRIT (Toulouse, France)
by jose moreno 21 Jun '22

21 Jun '22

PhD positions at IRIT (Toulouse, France) Topic 1 - Knowledge-aware few shot learning Topic 2 - Information retrieval models for structured and verbose queries Starting date: Fall 2022 Research team: IRIS at IRIT : Lynda Tamine / José G Moreno / Taoufiq Dkaki Synergie: Christophe Thovex Profile: - Master's level or engineering school in Computer Science, with skills in Information Extraction/Research and Text Mining - Good English skills (written and oral) - Good skills in advanced programming (Python, Pytorch, sklearn, ...) - Good knowledge in Machine Learning, deep learning is a plus Funding: Total gross salary for 3 years : 105 401,50 € / Note that final monthly salary depends of personal situation in France Application instructions: All applications must include the following to be considered: detailed CV, cover letter, transcripts (with rankings), contacts for recommendation. Please use “PhD application - Synergie” as subject of the email and select a preferred topic (1 or 2) if any. Applications to be sent by mail to Lynda Tamine, José G Moreno, and Taoufiq Dkaki (lynda.tamine(a)irit.fr, jose.moreno(a)irit.fr, taoufiq.dkaki(a)irit.fr). All applications will be processed as they arise until the positions will be filled. Location: Institut de Recherche en Informatique de Toulouse (IRIT) Université Toulouse 3 Paul Sabatier (UT3) 118 Route de Narbonne, F-31062 TOULOUSE CEDEX 9, France Comment: Nantes (France) is also an alternative location if requested by the applicant as Synergie has also offices in Nantes. More information in the attached PDF.

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