- Corpora - ELRA lists

GailBot usage: Jeffersonian annotations for CA
by Andrew Mitchell 21 Jul '22

21 Jul '22

Hi list A bit of a random and potentially off-list query, but has anybody any experience in using GailBot for Jeffersonian annotations and mark ups to facilitate CA on audio transcriptions? If so, please write to me off-list because I'd really appreciate any tips and tricks on how to use it. Cheers Andrew ----- Andrew Mitchell PhD, MSc, MBPsS Course Leader: Engineering Management (H1N171) Module Leader: Engineering Business Environment (ENGT5219) Web of Science Researcher ID: AAT-8116-2021 Agile PM Practitioner: 1000728878 Room QB1.15D , Institute of Energy & Sustainable Development (IESD) Faculty of Computing, Engineering & Media, De Montfort University Leicester LE1 9BH +44(0)116 257 7957 https://orcid.org/0000-0002-3946-4094

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Shared Task on Propaganda Detection
by Salam Khalifa 21 Jul '22

21 Jul '22

Hello All, We are pleased to announce the *Propaganda Detection in Arabic Social Media Text* shared task as part of the Workshop on Arabic NLP WANLP2022 <https://sites.google.com/view/wanlp2022/>. *Summary:* Propaganda is defined as an expression of opinion or action by individuals or groups deliberately designed to influence the opinions or actions of other individuals or groups with reference to predetermined ends. This is achieved by means of well-defined rhetorical and psychological devices. Currently, propaganda (or persuasion) techniques have been commonly used on social media to manipulate or mislead social media users. More detail on the techniques can be found here: https://propaganda.qcri.org/annotations/definitions.html. The goal of the shared task is to build models for identifying such techniques in Arabic social media text (specifically tweets). *Organizers:* Firoj Alam, Hamdy Mubarak, Wajdi Zaghouani, Preslav Nakov, and Giovanni Da San Martino. To find more information about the shared task please visit: https://sites.google.com/view/propaganda-detection-in-arabic Looking forward to your participation, Salam Khalifa (On behalf of the WANLP publicity chairs) -- *Salam Khalifa* *PhD Student* *Linguistics Department* *Institute for Advanced Computational Science (IACS)* *Stony Brook University* salam.khalifa(a)stonybrook.edu

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Shared Task on Gender Rewriting [Registration Deadline August 7th]
by Salam Khalifa 20 Jul '22

20 Jul '22

Hello All, We are pleased to announce the *Gender Rewriting* shared task as part of the Workshop on Arabic NLP WANLP2022 <https://sites.google.com/view/wanlp2022/>. *Summary:* The task of gender rewriting refers to generating alternatives to a given sentence to match different target user gender contexts (e.g., a female speaker with a male listener, a male speaker with a male listener, etc.). This requires changing the grammatical gender (masculine or feminine) of certain words referring to the users (speaker/1st person and listener/2nd person). In this task, we focus on Arabic, a gender-marking morphologically rich language. The task of gender rewriting was introduced by Alhafni et al. (2022). *Organizers: *Bashar Alhafni, Ossama Obeid, Houda Bouamor, and Nizar Habash. To find more information about the shared task please visit: https://sites.google.com/view/arabic-gender-rewriting/ Looking forward to your participation, Salam Khalifa (On behalf of the WANLP publicity chairs) -- *Salam Khalifa* *PhD Student* *Linguistics Department* *Institute for Advanced Computational Science (IACS)* *Stony Brook University* salam.khalifa(a)stonybrook.edu

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3rd CFP - SIMBig 2022, Peru (AI, Data Science, NLP, ML)
by Juan Antonio Lossio-Ventura 20 Jul '22

20 Jul '22

> [Apologies for cross-posting] > > ================================================================= > 3rd CALL FOR PAPERS - SIMBig 2022 > ================================================================= > > SIMBig 2022 - 9th International Conference on Information Management and Big Data > Where: Universidad Nacional Mayor de San Marcos, Lima, PERU > When: November 16 - 18, 2022 > Website: http://simbig.org/SIMBig2022/ <http://simbig.org/SIMBig2022/> > > ================================================================= > > OVERVIEW > ---------------------------------- > > SIMBig 2022 seeks to present new methods of Artificial Intelligence (AI), Data Science, and related fields, for analyzing, managing, and extracting insights and patterns from large volumes of data. > > > KEYNOTE SPEAKERS > ------------- > > Leman Akoglu, Carnegie Mellon University, USA > Jiang Bian, University of Florida, USA > Rich Caruana, Microsoft, USA > Dilek Hakkani-Tur, Amazon Alexa AI, USA > Monica Lam, Stanford University, USA > Wang-Chiew Tan, Facebook AI, USA > Andrew Tomkins, Google, USA > Bin Yu, University of California, Berkeley, USA > > IMPORTANT DATES > ------------- > > August 05, 2022 --> Papers submission deadline > September 09, 2022 --> Notification of acceptance > October 07, 2022 --> Camera-ready versions > November 16 - 18, 2022 --> Conference held in Lima, Peru > > PUBLICATION AND TRAVEL AWARDS > ------------- > > All accepted papers of SIMBig 2022 (tracks including) will be published with Springer CCIS Series <https://www.springer.com/series/7899>. > > > The best 8-10 papers of SIMBig 2022 (tracks including) will be selected to submit an extension to be published with the Springer SN Computer Science Journal. <https://www.springer.com/journal/42979> > > Thanks to the support of the North American Chapter of the Association for Computational Linguistics (NAACL) <http://naacl.org/>, SIMBig 2022 will offer 4 student travel awards for the best papers. > > > > TOPICS OF INTEREST > ------------- > > SIMBig 2022 has a broad scope. We invite contributions on theory and practice, including but not limited to the following technical areas: > > Artificial Intelligence > Data Science > Machine Learning > Natural Language Processing > Semantic Web > Healthcare Informatics > Biomedical Informatics > Data Privacy and Security > Information Retrieval > Ontologies and Knowledge Representation > Social Networks and Social Web > Information Visualization > OLAP and Business intelligence > Data-driven Software Engineering > > SPECIAL TRACKS > ------------- > > SIMBig 2022 proposes three special tracks in addition to the main conference: > > ANLP <https://simbig.org/SIMBig2022/en/anlp.html> - Applied Natural Language Processing > DISE <https://simbig.org/SIMBig2022/en/dise.html> - Data-drIven Software Engineering > SNMAM <https://simbig.org/SIMBig2022/en/snmam.html> - Social Network and Media Analysis and Mining > > CONTACT > ------------- > > SIMBig 2022 General Chairs > > Juan Antonio Lossio-Ventura, National Institutes of Health, USA (juan.lossio(a)nih.gov <mailto:juan.lossio@nih.gov>) > Hugo Alatrista-Salas, Pontificia Universidad Católica del Perú, Peru (halatrista(a)pucp.pe <mailto:halatrista@pucp.pe>) >

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PhD position in NLP/ML (Munich+Vienna)
by Benjamin Roth 20 Jul '22

20 Jul '22

The research group Data Mining and Machine Learning at the University of Vienna, in a continued collaboration with the Volkswagen Natural Language Processing Expert Center in Munich, is looking for graduates or advanced MSc students in Computer Science, Computational Linguistics, Statistics, or related fields, that are interested in doing a PhD in Machine Learning and Natural Language Processing. The PhD candidate would be expected to be located in Munich for the duration of the doctoral project. Please contact Prof. Benjamin Roth, Univ. of Vienna, (benjamin.roth(a)univie.ac.at, use subject "ml nlp phd", please include a short CV) to learn more about this opportunity. -- Univ.-Prof. Dr. Benjamin Roth Digitale Textwissenschaften Universität Wien Kolingasse 14 Raum 5.17 1090 Wien email: benjamin.roth(a)univie.ac.at tel: +43 14277 79513 video call: https://univienna.zoom.us/j/93796507934?pwd=VFg5dW9JbStPUml6WFVtOWJXV3phQT09 web: https://dm.cs.univie.ac.at/team/person/112089/

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[2nd CfP] Shared Task on Detecting Entities in the Astrophysics Literature (DEAL) at AACL-IJCNLP 2022
by Tirthankar Ghosal 20 Jul '22

20 Jul '22

***Shared Task: Detecting Entities in the Astrophysics Literature (DEAL)*** ***Website: https://ui.adsabs.harvard.edu/WIESP/2022/SharedTasks *** ***Twitter: https://twitter.com/wiesp_nlp *** A good amount of astrophysics research makes use of data coming from missions and facilities such as ground observatories in remote locations or space telescopes, as well as digital archives that hold large amounts of observed and simulated data. These missions and facilities are frequently named after historical figures or use some ingenious acronym which, unfortunately, can be easily confused when searching for them in the literature via simple string matching. For instance, Planck can refer to the person, the mission, the constant, or several institutions. Automatically recognizing entities such as missions or facilities would help tackle this word sense disambiguation problem. The shared task consists of Named Entity recognition (NER) on samples of text extracted from astrophysics publications. The labels were created by domain experts and designed to identify entities of interest to the astrophysics community. They range from simple to detect (ex: URLs) to highly unstructured (ex: Formula), and from useful to researchers (ex: Telescope) to more useful to archivists and administrators (ex: Grant). Overall 31 different labels are included, and their distribution is highly unbalanced (ex: ~100x more Citations than Proposals). Submissions will be scored using both the CoNLL-2000 shared task seqeval F1-Score at the entity level, and scikit-learn's Matthews correlation coefficient method at the token level. We also encourage authors to propose their own evaluation metrics. A sample dataset and more instructions can be found at: https://ui.adsabs.harvard.edu/WIESP/2022/SharedTasks Participants (individuals or groups) will have the opportunity to present their findings during the workshop and write a short paper. The best performant or interesting approaches might be invited to further collaborate with the NASA Astrophysics Data System ( https://ui.adsabs.harvard.edu/). The DEAL shared task is a part of the *1st Workshop on Information Extraction from Scientific Publications (WIESP) at AACL-IJCNLP 2022: * https://ui.adsabs.harvard.edu/WIESP/2022/ ***Please fill in this form to report your intention to participate in the shared task*** https://forms.office.com/r/KKpeKJBLy3 ***Shared Task Submission*** Link to data and scoring scripts: https://huggingface.co/datasets/fgrezes/WIESP2022-NER CodaLab Link to the online competition : https://codalab.lisn.upsaclay.fr/competitions/5062 ***Important Dates*** - Training+Validation Data Release: June 1, 2022 - Validation Phase: June 1 - July 31, 2022 - Test Data Release: August 1, 2022 - Final Scoring Period: August 1 - August 10, 2022 - System Report Submission: August 25, 2022 - Notification: September 25, 2022 - Camera-ready Submission Deadline: October 10, 2022 - Event Date: November 20, 2022 (online) ***All submission deadlines are 11.59 pm UTC -12h (“Anywhere on Earth”)*** ***Organizers*** - Tirthankar Ghosal <https://elitr.eu/tirthankar-ghosal>, Charles University, CZ - Sergi Blanco-Cuaresma <https://www.blancocuaresma.com/s/>, Center for Astrophysics | Harvard & Smithsonian, USA - Alberto Accomazzi <https://ui.adsabs.harvard.edu/about/team/team/aaccomazzi.html>, Center for Astrophysics | Harvard & Smithsonian, USA - Robert M. Patton <https://www.ornl.gov/staff-profile/robert-m-patton>, Oak Ridge National Laboratory, USA - Felix Grezes <https://ui.adsabs.harvard.edu/about/team/team/fgrezes.html>, Center for Astrophysics | Harvard & Smithsonian, USA - Thomas Allen <https://ui.adsabs.harvard.edu/about/team/team/tallen.html>, Center for Astrophysics | Harvard & Smithsonian, USA -- +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ Tirthankar Ghosal Researcher at UFAL, Charles University, CZ https://member.acm.org/~tghosal +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

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Entity Taggers
by Kims Shady 20 Jul '22

20 Jul '22

Hye all I need to tag entities and relationships between them in an English language corpus. Can someone please help which tool i am supposed to use for this? Any help would be appreciated. Regards

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Final call for papers: LoResMT 2022 (The 5th Workshop on Technologies for MT of Low-Resource Languages)
by Atul K. Ojha 20 Jul '22

20 Jul '22

___Apologies for cross-posting__ The Fifth Workshop on Technologies for Machine Translation of Low Resource Languages (LoResMT 2022) https://sites.google.com/view/loresmt @ COLING 2022 Gyeongju, Republic of Korea, October 12-17, 2022 SUBMISSION https://www.softconf.com/coling2022/LoResMT_2022/ TIMELINE *Submission Open Until*: *July 30, 2022 (Saturday) at 23:59 (Anywhere on Earth)* *Notification of acceptance: *August 22, 2022 (Monday) *Camera-ready papers due:* September 5, 2022 (Monday) *LoResMT workshop:* October 16, 2022 (online on Sunday at UTC+9) *COLING 2022: *October 12-17, 2022 SCOPE Based on the success of past low-resource machine translation (MT) workshops at AMTA 2018 (https://amtaweb.org/), MT Summit 2019 ( https://www.mtsummit2019.com), AACL-IJCNLP 2020 (http://aacl2020.org/), and AMTA 2021, we introduce the Fifth LoResMT workshop at COLING 2022. The workshop provides a discussion panel for researchers working on MT systems/methods for low-resource and under-represented languages in general. We would like to help review/overview the state of MT for low-resource languages and define the most important directions. We also solicit papers dedicated to supplementary NLP tools that are used in any language and especially in low-resource languages. Overview papers of these NLP tools are very welcome. It will be beneficial if the evaluations of these tools in research papers include their impact on the quality of MT output. TOPICS We are highly interested in (1) original research papers, (2) review/opinion papers, and (3) online systems on the topics below; however, we welcome all novel ideas that cover research on low-resource languages. - COVID-related corpora, their translations and corresponding NLP/MT systems - Neural machine translation for low-resource languages - Work that presents online systems for practical use by native speakers - Word tokenizers/de-tokenizers for specific languages - Word/morpheme segmenters for specific languages - Alignment/Re-ordering tools for specific language pairs - Use of morphology analyzers and/or morpheme segmenters in MT - Multilingual/cross-lingual NLP tools for MT - Corpora creation and curation technologies for low-resource languages - Review of available parallel corpora for low-resource languages - Research and review papers of MT methods for low-resource languages - MT systems/methods (e.g. rule-based, SMT, NMT) for low-resource languages - Pivot MT for low-resource languages - Zero-shot MT for low-resource languages - Fast building of MT systems for low-resource languages - Re-usability of existing MT systems for low-resource languages - Machine translation for language preservation SUBMISSION INFORMATION We are soliciting two types of submissions: (1) research, review, and position papers and (2) system demonstration papers. For research, review and position papers, the length of each paper should be at least four (4) and not exceed eight (8) pages, plus unlimited pages for references. For system demonstration papers, the limit is four (4) pages. Submissions should be formatted according to the official COLING 2022 style templates (LaTeX, Word, Overleaf). Accepted papers will be published online in the COLING 2022 proceedings and will be presented at the conference. Submissions must be anonymized and should be done using the official conference management system ( https://www.softconf.com/coling2022/LoResMT_2022/). Scientific papers that have been or will be submitted to other venues must be declared as such and must be withdrawn from the other venues if accepted and published at LoResMT. The review will be double-blind. We would like to encourage authors to cite papers written in ANY language that are related to the topics, as long as both original bibliographic items and their corresponding English translations are provided. Registration is handled by the main conference ( https://coling2022.org/coling). ORGANIZING COMMITTEE (LISTED ALPHABETICALLY) Atul Kr. Ojha, DSI, National University of Ireland Galway & Panlingua Language Processing LLP Chao-Hong Liu, Potamu Research Ltd Ekaterina Vylomova, University of Melbourne, Australia Jade Abbott, Retro Rabbit Jonathan Washington, Swarthmore College Nathaniel Oco, National University (Philippines) Tommi A Pirinen, UiT The Arctic University of Norway, Tromsø Valentin Malykh, Huawei Noah’s Ark lab and Kazan Federal University Varvara Logacheva, Skolkovo Institute of Science and Technology Xiaobing Zhao, Minzu University of China PROGRAM COMMITTEE (LISTED ALPHABETICALLY) Alberto Poncelas, Rakuten, Singapore Alina Karakanta, Fondazione Bruno Kessler Amirhossein Tebbifakhr, Fondazione Bruno Kessler Anna Currey, Amazon Web Services Aswarth Abhilash Dara, Amazon Arturo Oncevay, University of Edinburgh Atul Kr. Ojha, DSI, National University of Ireland Galway & Panlingua Language Processing LLP Bharathi Raja Chakravarthi, DSI, National University of Ireland Galway Beatrice Savold, University of Trento Bogdan Babych, Heidelberg University Chao-Hong Liu, Potamu Research Ltd Duygu Ataman, University of Zurich Ekaterina Vylomova, University of Melbourne, Australia Eleni Metheniti, CLLE-CNRS and IRIT-CNRS Francis Tyers, Indiana University Kalika Bali, MSRI Bangalore, India Koel Dutta Chowdhury, Saarland University (Germany) Jade Abbott, Retro Rabbit Jasper Kyle Catapang, University of the Philippines John P. McCrae, DSI, National University of Ireland Galway Liangyou Li, Noah’s Ark Lab, Huawei Technologies Maria Art Antonette Clariño, University of the Philippines Los Baños Mathias Müller, University of Zurich Nathaniel Oco, National University (Philippines) Rico Sennrich, University of Zurich Sangjee Dondrub, Qinghai Normal University Santanu Pal, WIPRO AI Sardana Ivanova, University of Helsinki Shantipriya Parida, Silo AI Sina Ahmadi, DSI, National University of Ireland Galway Sunit Bhattacharya, Charles University Surafel Melaku Lakew, Amazon AI Tommi A Pirinen, UiT The Arctic University of Norway, Tromsø Wen Lai, Center for Information and Language Processing, LMU Munich Valentin Malykh, Huawei Noah’s Ark lab and Kazan Federal University CONTACT Please email loresmt(a)googlegroups.com if you have any questions/comments/suggestions. PROCEEDINGS Proceedings of LoResMT 2021 Workshop https://aclanthology.org/volumes/2021.mtsummit-loresmt/ Proceedings of LoResMT 2020 Workshop https://aclanthology.org/volumes/2020.loresmt-1/ Proceedings of LoResMT 2019 Workshop https://www.aclweb.org/anthology/W19-6800 Slides of LoResMT 2018 Workshop presentations https://sites.google.com/view/loresmt-2018/ Proceedings of LoResMT 2018 Workshop https://amtaweb.org/amta-2018-proceedings-for-the-conference-workshops-and-…

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Shared Task on Lexical Simplification for English, Portuguese and Spanish / TSAR-2022 @EMNLP2022
by Horacio Saggion 20 Jul '22

20 Jul '22

Shared Task on Lexical Simplification for English, Portuguese and Spanish In conjunction with the TSAR-2022 Workshop @EMNLP2022 *** CALL FOR PARTICIPATION *** Lexical Simplification is the process of reducing the lexical complexity of a text by replacing difficult words with easier to read (or understand) expressions while preserving the original information and meaning. Lexical Simplification (LS) aims at facilitating reading comprehension to different target readerships such as foreign language learners, native speakers with low literacy levels, second language learners or people with different kinds of reading impairments. This new Lexical Simplification Shared Task features three similar datasets in three different languages: English, Brazilian Portuguese, and Spanish. Definition of the task Given a sentence containing a complex word, systems should return an ordered list of “simpler” valid substitutes for the complex word in its original context. The list of simpler words (up to a maximum of 10) returned by the system should be ordered by the confidence the system has in its prediction (best predictions first). The ordered list must not contain ties. An instance of the task for the English language is: 1. “That prompted the military to deploy its largest warship, the BRP Gregorio del Pilar, which was recently acquired from the United States.” Complex word: deploy For this instance a system may suggest the following ranked substitutes: send, move, position, redeploy, employ, situate… Systems should only produce simplifications that are good contextual fits (semantically and syntactically). Participating teams can register (details below) for three different tracks, one per language. * English monolingual (EN) * Portuguese (Brazilian) monolingual (PT-BR) * Spanish monolingual (ES) It is possible to participate in one, two or all three tracks. Participating teams will be allowed to submit up to 3 runs per track. Data The three datasets (trial data with gold annotations and test data without gold annotations)and the evaluation script will be available through a GitHub repository. There is no training dataset. However, a sample of 10 or 12 instances with gold standard annotations will be provided. Note that participating teams are allowed to use any other lexical simplification datasets or resources for developing their systems. Test data with gold annotations will also be released via the same GitHub repository at the end of the evaluation period. Evaluation Metrics The evaluation metrics to be applied in the TSAR-2022 Shared Task are the following: MAP@K (Mean Average Precision @ K): K={1,3,5,10}. The MAP@K metric is used commonly for evaluating Information Retrieval models and Recommender Systems. For this Lexical Simplification task, instead of using a ranked list of relevant and irrelevant documents to evaluate our ranking output, we use a ranked list of predicted substitutes, which can be matched (relevant) and not matched (irrelevant) terms against the set of the gold-standard annotations for evaluation. The traditional Precision metric, in the context of Lexical Simplification, can be used to see how many of the predicted substitutes are relevant. But precision fails to capture the order in which correctly predicted substitutes are. Mean Average Precision is designed to work for binary relevance: candidates that match or not in the list of gold annotations. So MAP@K for Lexical Simplification evaluates the following aspects: 1) are the predicted substitutes relevant?, and 2) are the predicted substitutes at the top positions? Potential@K: K={1,3,5,10}. The percentage of instances for which at least one of the substitutions predicted is present in the set of gold annotations. Accuracy@K@top1: K={1,2,3}. The ratio of instances where at least one of the K top predicted candidates matches the most frequently suggested synonym/s in the gold list of annotated candidates. Note 1: Potential@1/MAP@1/Precision@1 will give the same value. Note 2: The exact computation of the metrics will be provided in the official evaluation script. Publication Participating teams will be invited to submit system description papers (four pages with an unlimited number of pages for references) which will be peer-reviewed by at least 2 reviewers (at least one member of each participating team will be required to help with the review process) and papers will be published in the TSAR-2022 Workshop proceedings. The submissions will be via SoftConf and details for submission will be communicated to registered teams in due time. Important dates * Registration opens: July 19th, 2022 * Release of sample/trial instances with gold annotations: July 20th, 2022 * Release of evaluation metrics and code: July 22th, 2022 * Registration deadline: September 7, 2022 * Test set release (without gold annotations): September 8, 2022 * Submissions of systems' output due: September 15, 2022 * Official results announced: September 30, 2022 * Test set release (wit gold annotations): September 30, 2022 * Submission of Shared Tasks papers deadline: October 15, 2022 * Shared Task Papers Reviews due: November 1, 2022 * Camera-ready deadline for Shared-task papers: November 10, 2022 * TSAR Workshop and Shared Task: December 8, 2022 Registering your team: Please access this form to register for the TSAR-2022 Shared Task on Lexical Simplification. https://forms.gle/6iNm5cTRueA78ri17 Website and Shared Task Guidelines Please visit the TSAR-2022 Shared Task website to obtain further information about the Guidelines, Datasets, and team registration. https://taln.upf.edu/pages/tsar2022-st Organisers * Horacio Saggion, Chair in Computer Science and Artificial Intelligence and Head of the LaSTUS Lab in the TALN-DTIC, Universitat Pompeu Fabra * Sanja Štajner, Senior Research Scientist and R&D Application Manager at Symanto Research * Matthew Shardlow, Senior Lecturer at Manchester Metropolitan University * Marcos Zampieri, Assistant Professor at the Rochester Institute of Technology * Daniel Ferrés, Post-Doctoral Research Assistant at LaSTUS Lab. at TALN-DTIC, Universitat Pompeu Fabra * Kai North, PhD student at the Rochester Institute of Technology * Kim Cheng Sheang, PhD student at LaSTUS Lab. at TALN-DTIC, Universitat Pompeu Fabra -- Professor Horacio Saggion Head of the Large Scale Text Understanding Systems Lab Full Professor / Chair in Computer Science and Artificial Intelligence TALN / DTIC Universitat Pompeu Fabra [image: https://twitter.com/h_saggion] [image: https://www.linkedin.com/in/horacio-saggion-1749b916]

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[CfP] 2nd SummDial: A SemDial 2022 Special Session on Summarization of Dialogues and Multi-Party Meetings
by Tirthankar Ghosal 20 Jul '22

20 Jul '22

Dear Colleague, We cordially invite you to participate and submit your paper in the 2nd SummDial @ SemDial 2022 ( https://elitr.github.io/automatic-minuting/summdial-2022.html). The First SummDial special session was held at SIGDial 2021 and witnessed enthusiastic participation from the dialogue, summarization, NLP, and speech communities. https://elitr.github.io/automatic-minuting/summdial.html https://dl.acm.org/doi/10.1145/3527546.3527561 (Report on SummDial @ SIGDial 2021) The second SummDial would be held as a hybrid session at SemDial 2022 ( https://semdial2022.github.io/). Papers accepted in SummDial will appear in the SemDial Anthology (http://semdial.org/anthology/venues/semdial/). ***2nd SummDial: A SemDial 2022 <https://semdial2022.github.io/#> Special Session on Summarization of Dialogues and Multi-Party Meetings*** ***Website: https://elitr.github.io/automatic-minuting/summdial-2022.html *** ***Submission Deadline: August 1, 2022 *** ***Event Date: August 24, 2022 *** With a sizeable working population of the world going virtual, resulting in information overload from multiple online meetings, imagine how convenient it would be to just hover over past calendar invites and get concise summaries of the meeting proceedings? How about automatically minuting a multimodal multi-party meeting? Are minutes and multi-party dialogue summaries the same? We believe Automatic Minuting is challenging. There are possibly no agreed-upon guidelines for taking minutes, and people adopt different styles to record meeting minutes. The minutes also depend on the meeting's category, the intended audience, and the goal or objective of the meeting. We hosted the First SummDial Special Session at SIGDial 2021. Several significant problems and challenges in multi-party dialogue and meeting summarization came from the discussions in the first SummDial, which we documented in our event report <https://dl.acm.org/doi/10.1145/3527546.3527561>. Since we witnessed enthusiastic participation of the dialogue and summarization community in the first SummDial special session <https://elitr.github.io/automatic-minuting/summdial.html> ( https://elitr.github.io/automatic-minuting/summdial.html), we are hosting the Second SummDial special session at SemDial 2022 <https://semdial2022.github.io/#> (https://semdial2022.github.io/#). This year, we intend to continue discussing these challenges and lessons learned from the previous SummDial. Our goal for this special session would be to stimulate intense discussions around this topic and set the tone for further interest, research, and collaboration in both Speech and Natural Language Processing communities. Our topics of interest are Dialogue Summarization, including but not limited to Meeting Summarization, Chat Summarization, Email Threads Summarization, Customer Service Summarization, Medical Dialogue Summarziation, and Multi-modal Dialogue Summarization. Our shared task on Automatic Minuting (AutoMin) at Interspeech 2021 ( https://elitr.github.io/automatic-minuting/) was another community effort in this direction. ***Call for papers*** We invite regular and work-in-progress papers that report: - Current research in multi-party dialogue summarization for summarizing meetings, spoken dialogue, using speech, text, or multi-modal data (audio, video), - Challenges in dialogue summarization evaluation (manual + automatic), - New methods and metrics for dialogue summarization evaluation, - Relevant corpus collection, pre-processing, development, and ethical issues involved, - Compare and contrast speech-specific systems to systems imported from text summarization, - Tools for meeting transcript generation and automatic summarization, - Topic detection and span identification in meeting transcripts for multi-topic summarization, - Position papers to reflect on the current state of the art in this topic, to take stock of where we have been, where we are, where we are going and where we should go. Researchers may choose to submit: - ***Long papers*** Authors should submit an anonymous paper of at most 8 pages of content (up to 2 additional pages are allowed for references). - ***Short papers*** Authors should submit a non-anonymized paper of at most 2 pages of content (up to 1 additional page allowed for references). Submissions to this track can be non-archival on request. - ***Position Papers*** Including extended abstracts, work-in-progress, and late-breaking papers. ***Submission Link*** https://easychair.org/my/conference?conf=summdial2022 Submissions should follow the ACL format. Papers that have been or will be submitted to other meetings or publications must provide this information using a footnote on the title page of the submissions. SummDial 2022 cannot accept work for a publication that will be (or has been) published elsewhere. ***Special Session Program*** The special session would consist of a keynote, a panel, oral and/or poster paper presentations. ***Organizers*** - Tirthankar Ghosal <https://elitr.eu/tirthankar-ghosal/>, Institute of Formal and Applied Linguistics, Charles University, Czech Republic - Muskaan Singh, IDIAP, Switzerland - Xinnou Xu, University of Edinburgh, UK - Ondřej Bojar <https://ufal.mff.cuni.cz/ondrej-bojar>, Institute of Formal and Applied Linguistics, Charles University, Czech Republic -- Tirthankar Ghosal, Researcher, EU H2020 Project ELITR <https://elitr.eu/> and NEUREM3 <https://ufal.mff.cuni.cz/grants/neurem3> Institute of Formal and Applied Linguistics <https://ufal.mff.cuni.cz/home-page>, Charles University <https://cuni.cz/uken-1.html>, Czech Republic https://member.acm.org/~tghosal

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2025

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2022

Corpora