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Webminar by Elena Sokolova (Amazon Text-to-Speech Group)
by HiTZ zentroa 31 Oct '24

31 Oct '24

**** We apologize for the multiple copies of this email. In case you are already registered to the next webinar, you do not need to register again. **** ------------------------------------------------------------------------ Dear colleague, We are happy to announce the next webinar in the Language Technology webinar series organized by the HiTZ Chair of AI&LT (https://hitz.eus). You can check the videos of previous webinars and the schedule for upcoming webinars here: http://www.hitz.eus/webinars Next webinar: *Speaker:* Elena Sokolova (Amazon Text-to-Speech Group) *Title:* How we do research in Speech at Amazon *Date: * Thursday, November 7, 2024 - 15:00 CET *Summary:* In this talk we will present how Speech technology has developed in the past 20 years. We will take a dive deep into the research that we do at Amazon in our Text-to-Speech lab, describe the challenges that we face and how we solve them at scale. We will also give an overview of the internship opportunities we have in our department for those of you who want to join our team in 2025. *Bio:* Elena is a Machine Learning team manager at Amazon, where she leads novel research in the field of speech technology. Over the past five years, she has overseen the deployment of machine learning projects into production and collaborated with her team to publish cutting-edge research on text-to-speech technology. Before joining Amazon, Elena completed her PhD at Radboud University Nijmegen in the Netherlands and gained industry experience as a Senior Machine Learning Scientist at Booking.com. * Upcoming webinars:* · Javier de la Rosa (December 12, 2024) · Ekaterina Shutova (January 30, 2025) · Sebastian Ruder (February 6, 2025) 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 P.S: HiTZ will not grant any type of certificate for attendance at these webinars.

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2 year post-doc position available at Lattice (Paris/Montrouge, France) F/H
by Thierry Poibeau 31 Oct '24

31 Oct '24

Offer Description For the full description, see: https://euraxess.ec.europa.eu/jobs/286797 for the online description. Lattice is offering a two-year postdoctoral position starting on January 1, 2025 or soon thereafter. Candidates may choose from one of the following research topics: 1. AI and Society: impact of AI on society, including topics such as climate effects, changes in the workforce, issues around data access (especially. copyright challenges linked to large models), and more. 2. Interpretation of Large Language Models (LLMs) from a Linguistic Perspective: relationship between LLMs and linguistic theory. Topics might include what LLMs reveal about language, their (non) alignment with linguistic theory, etc. 3. Cultural Analytics Using Large Corpora: cultural analysis based on large datasets, particularly those from the Bibliothèque nationale de France (which include literature, but also newspapers and francophone web archives). The successful candidate should have completed their PhD in recent years or be close to completion. A relevant publication track record in a related field is required. To apply, please submit a CV (including a list of publications), a short research proposal (2–4 pages), a link to one or two relevant publications, and the names of two references to thierry.poibeau(a)ens.psl.eu by November 15, 2024. The proposal should clearly outline the research topic and the intended methodology. I can briefly answer questions (for ex. on the adequacy of a research topic), but the successful candidate will be selected through interviews after Nov 15. The position is based in Montrouge and Paris (Montrouge is a 5-minute walk from the Mairie de Montrouge metro station). Salary will be commensurate with experience and follows the ENS salary scale. Lattice is an interdisciplinary lab conducting research in linguistics, natural language processing, and computational humanities. International profiles are strongly encouraged to apply. Mastering French is a plus, but is not mandatory.

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PhD positions in Research Training Group KEMAI on Medical XAI
by Christiane Böhm 31 Oct '24

31 Oct '24

Dear all, starting January 2025, 9 doctoral positions are available within our DFG Research Training Group KEMAI (Knowledge Infusion and Extraction for Explainable Medical AI) at Ulm University in Germany. The KEMAI team aims at combining the benefits of knowledge- and learning-based systems, to not only allow for state-of-the-art accuracy in medical diagnosis, but to also clearly communicate the obtained predictions to physicians, considering ethical implications within the medical decision process. KEMAI’s main purpose is to interdisciplenarily train PhD students from computer science, medicine, and ethics in the area of explainable medical AI. The RTG offers a structured doctoral program that creates an environment in which young scientists can conduct research at the highest level in the field of medical AI. We invite highly motivated candidates with a passion for research and a desire to contribute to an interdisciplinary academic environment to apply for these positions. (The positions are fully funded for 3+1 years and come with an E13 salary.) Projects include: Data Exploitation • A1 – Harvesting Medical Guidelines using Pre-trained Language Models Project Leads: Prof. Scherp (Computer Science), Prof. Braun (Computer Science), Dr. Vernikouskaya (Medicine) This project focuses on researching multimodal pre-trained language models (LM) that extract symbolic knowledge on medical diagnosis and treatments from input documents. The models will incorporate structured knowledge and represent extracted information using an extended process ontology. The project applies these models to COVID-19 related imaging and treatments, contributing to OpenClinical’s COVID-19 Knowledge reference model and adapting to various benchmarks. • A2 – Stability Improved Learning with External Knowledge through Contrastive Pre-training Project Leads: Jun.-Prof. Götz (Medicine), Prof. Scherp (Computer Science) This project aims to improve machine learning reliability in small data settings by learning from disconnected datasets using contrastive learning. It investigates if contrastive learning can reduce classifier susceptibility to confounders, reverse confounding effects, and identify out-of-distribution test samples. The project seeks to find approaches that address these technical challenges. Knowledge Infusion • B2 – Semantic Design Patterns for High-Dimensional Diagnostics Project Leads: Prof. Kestler (Medicine), Prof. M. Beer (Medicine) This project defines semantic design patterns for incorporating SemDK in ML algorithms to improve clinical predictions and tumor characterization. The patterns will be categorized by their mechanisms and knowledge representation, providing guidelines for application. The project evaluates these patterns in image analysis and molecular diagnostics based on high-dimensional data. Knowledge Extraction • C2 – Learning Search and Decision Mechanisms in Medical Diagnoses Project Leads: Prof. Neumann (Computer Science), Jun.-Prof. Götz (Medicine) This project studies human attentive search and object attention principles for vision-based medical diagnosis. It investigates mechanisms of object-based attention and visual routines for task execution. The goal is to formalize human visual search strategies and integrate them into deep neural networks (DNNs) for improved medical diagnosis. Model Explanation • D1 – Accountability of AI-based Medical Diagnoses Project Leads: Prof. Steger (Ethics), Prof. Ropinski (Computer Science) This project addresses the ethical analysis of AI system designs for medical diagnoses. It focuses on determining which AI-supported processes need to be explainable and transparent, generating comprehensive information to help users understand AI-driven medical decisions. • D2 – Explainability, Understanding, and Acceptance Requirements Project Leads: Prof. Hufendiek (Philosophy), Prof. Glimm (Computer Science), Dr. Lisson (Medicine) This project applies philosophical insights on understanding and explanations to the use of AI in medical diagnosis. It clarifies the roles of understanding and abductive reasoning in medical diagnosis, identifies conflicts between stakeholders, and suggests ways to develop and integrate AI explanations with human experts' reasoning processes. Medical PhD Projects (10 months) The outlined PhD projects are complemented by medical PhD projects, which complement the technical and ethical projects, and which are targeted towards medical researchers. For further information on KEMAI and application please go to https://kemai.uni-ulm.de/ Best regards Christiane Böhm - Coordinator - RTG KEMAI Ulm University James-Franck-Ring - O27 Room 321 D-89081 Ulm Germany phone: +49 731 50 31321 christiane.boehm(a)uni-ulm.de kemai(a)uni-ulm.de

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[CfP] "Contemporary Trends in English-Language Studies 2" (Zielona Góra)
by Leszek Szymański 30 Oct '24

30 Oct '24

Dear Colleagues,the Institute of Modern Languages at the University of Zielona Góra announces the "Contemporary Trends in English-Language Studies 2" conference. This year's edition will be held in a hybrid mode on April 3-4, 2025.The abstract submission deadline is January 31, 2025.The 2025 edition of the conference is organized under the honorary patronage of the Polish Linguistic Society.More information is available at: https://sites.google.com/view/ctiels/ Thank you!Leszek Szymański

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Call for Papers: NAACL Student Research Workshop (SRW) 2025
by mengyan3＠andrew.cmu.edu 30 Oct '24

30 Oct '24

### Call for Papers The Student Research Workshop (SRW) provides a venue for student researchers to present their work in computational linguistics and natural language processing. Students receive feedback from the general conference audience as well as from mentors specifically assigned according to the topic of their work. We invite papers in two different categories: <b>Research Papers </b>: Papers in this category can describe completed work, or work in progress with preliminary results. For these papers, the first author must be a current student. Topics of interest for the SRW are the same as NAACL main conference. See the list of topics here. <b>Thesis Proposals</b>: This category is appropriate for advanced students who have decided on a thesis topic and wish to get feedback on their proposal and broader ideas for their continuing work. ### Submissions Papers should be submitted on OpenReview by December 1, 2024. OpenReview link: https://openreview.net/group?id=aclweb.org/NAACL/2024/Workshop/Student_Rese… Submissions should be no more than five pages not including references. Make sure to read through the Author Guidelines on our website for detailed submission instructions. Not following the author guidelines may result in your paper being desk-rejected. Please use the standard ACL templates and style guide, though please keep in mind that the SRW page limit is 5 pages (not including references) ACL templates: https://github.com/acl-org/acl-style-files ### Website Please see our CfP on the website for more details: https://naacl2025-srw.github.io/cfp

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[Deadline Extension] Call for Shared Task Proposals for RANLP 2025
by Ranasinghe, Tharindu 30 Oct '24

30 Oct '24

[Due to many requests, we have extended the deadlines.] We invite proposals for tasks to be run as part of RANLP 2025 (Recent Advances in Natural Language Processing): https://ranlp.org/ranlp2025/.<https://ranlp.org/ranlp2025/> RANLP is one of the most influential and competitive NLP conferences. RANLP 2025 will take place in September 2025 at the Black Sea city of Varna. For the first time in RANLP history, we are organising a shared task campaign as part of the main conference and inviting task organisers to submit their task proposals. Researchers and practitioners from all areas of Natural Language Processing and related communities are invited to submit task proposals. For RANLP 2025, we welcome any task that can evaluate an automatic system for natural language processing. We especially encourage tasks for languages other than English, multi-lingual tasks, and tasks that develop novel applications of natural language processing. We strongly encourage proposals based on already published datasets, as this can provide concrete examples and help minimise the challenges of organising the shared task. In the event of receiving many proposals, preference will be given to proposals based on already published datasets. If you are unsure whether a task is suitable, please contact the shared task chairs to discuss your idea. Task Selection Task proposals will be reviewed by at least two reviewers, and the reviews will serve as the basis for acceptance decisions. Task proposals will be evaluated on: * Novelty - Is the task based on a new problem that has not been explored much in the community? If similar tasks have been organised before, does this task cover new languages/ domains? * Data – Is the data available and published already? Do annotations have meaningfully high inter-annotator agreements? Have all appropriate licenses for the use and re-use of the data been secured? * Evaluation—Is the evaluation methodology sound? Is there an automated platform for the evaluation (e.g., CodaLab, Kaggle)? Task Organisation We specifically welcome task proposals from early career researchers. However, we strongly encourage tasks that have a diverse team of organisers as that will ease the task organisation. Apart from providing a dataset, task organisers are expected to: 1. Verify data quality in terms of annotator agreement. 2. Verify licenses for the data to allow its use in the competition. 3. Provide task participants with baseline systems. 4. Create a CodaLab or other similar evaluation platform for the task and manage automatic evaluation. 5. Promote the task within the target research community. 6. Manage and organise review process of participants’ submissions of system description papers. 7. Write a task description paper to be included in RANLP proceedings. 8. Contribute to the tasks overview paper written by shared task chairs and other task organisers which will also be included in RANLP proceedings. 9. Register and present the shared task description paper at RANLP 2025 on either 11th or 12th September 2025 (the exact date will be confirmed later) Important Dates * Task proposals due - November 20, 2024 * Task selection notification – November 25, 2024 Recommended Timeline for the Tasks * Sample data and task website ready - December 1, 2024 * Training data ready - December 15, 2024 * Evaluation data ready - March 1, 2025 * Evaluation starts – March 10, 2025 * Evaluation end - March 31, 2025 (latest date; task organisers may choose an earlier date) * Paper submission due – April 20, 2025 * Notification to authors – May 16, 2025 * Task overview paper due – May 25, 2025 * Camera-ready due - May 31, 2025 * Shared task presentation co-located with RANLP 2025 – September 11 and September 12, 2025 Tasks that do not meet critical deadlines, such as those for launching the task, setting up the CodaLab website, and uploading samples, training, and evaluation data, may be cancelled at the discretion of the shared task chairs. Submission Details The task proposal should be a self-contained document of no longer than 2 pages (plus additional pages for references). All submissions must be in PDF format, following the RANLP 2023 template available at https://ranlp.org/ranlp2023/index.php/submissions/ Each proposal should contain the following: * Overview * Summary of the task – What is the goal of the task * Expected number of participants and justification * Data & Resources * How the training/testing data will be produced. Discuss whether the dataset is already published * Details of license, so that the data can be used by the research community * How much data will be produced * How data quality will be ensured and evaluated * An example of what the data would look like * Evaluation * The evaluation methodology to be used, including clear evaluation criteria - * The evaluation platform (i.e. CodaLab, Kaggle etc.) * Task organisers * Names, affiliations, email addresses * brief description of relevant experience or expertise The submissions should be done via START - https://softconf.com/ranlp25/papers/user/scmd.cgi?scmd=submitPaperCustom&pa… Proceedings Tasks overview paper, task description papers and participant papers will be published as part of RANLP 2025 proceedings in ACLAnthology. Task organisers and participants are expected to attend RANLP 2025 on September 11 and September 12, 2025, and present their work in order to include it in the proceedings. Shared Task Chairs Dr Tharindu Ranasinghe, Lancaster University, UK Dr Saad Ezzini, Lancaster University, UK RANLP 2024 Chairs Programme Committee Chair: Prof Dr Ruslan Mitkov, Lancaster University, UK Organising Committee Chair: Prof Dr Galia Angelova, Bulgarian Academy of Sciences, Bulgaria Best Regards Dr Tharindu Ranasinghe | Lecturer in Security and Protection Science School of Computing and Communications | Lancaster University Contact me on Teams<https://teams.microsoft.com/l/chat/0/0?users=t.ranasinghe@lancaster.ac.uk> www.lancaster.ac.uk<https://www.lancaster.ac.uk/>

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PostDoc at TurkuNLP (NLP/Digital linguistics)
by Veronika Laippala 30 Oct '24

30 Oct '24

Dear colleagues, TurkuNLP at the University of Turku, Finland, has a fully funded open PostDoc position in NLP / digital linguistics. Term: 1.12.2024-31.12.2025 (or upon agreement) Deadline: 18.11. 2024 Topics: Web register (genre) identification / web register studies, processing massively multilingual web crawls, massively multilingual cross-linguistic comparisons of web registers. Detailed information: https://ats.talentadore.com/apply/tutkijatohtorin-projektitutkijan-maaraaik… Please contact me for further information. Applications must be made using the Talentadore system linked above. Best, Veronika Laippala Professor of Digital linguistics, TurkuNLP

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CfP: Workshop on Generative AI and Knowledge Graphs (GenAIK) co-located with COLING 2025 (with some travel grant for 2 students)
by Genet Asefa Gesese 28 Oct '24

28 Oct '24

--------------------------------------------------------------------------------- Workshop on Generative AI and Knowledge Graphs (GenAIK), 19 January 2025, Abu Dhabi, UAE Web: https://genetasefa.github.io/GenAIK2025/ X: @GenAIK25 LinkedIn: https://www.linkedin.com/groups/9868047 Mastodon: https://sigmoid.social/@GenAIK --------------------------------------------------------------------------------- In conjunction with COLING 2025, January 19-24 --------------------------------------------------------------------------------- Workshop Overview --------------------------------------------------------------------------------- Generative Artificial Intelligence (GenAI) is a branch of artificial intelligence capable of creating seemingly new, meaningful content, including text, images, and audio. It utilizes deep learning models, such as Large Language Models (LLMs), to recognize and replicate data patterns, enabling the generation of human-like content. Notable families of LLMs include GPT (GPT-3.5, GPT-3.5 Turbo, and GPT-4), LLaMA (LLaMA and LLaMA-2), and Mistral (Mistral and Mixtral). GPT, which stands for Generative Pretrained Transformer, is especially popular for text generation and is widely used in applications like ChatGPT. GenAI has taken the world by storm and revolutionized various industries, including healthcare, finance, and entertainment. However, GenAI models have several limitations, including biases from training data, generating factually incorrect information, and difficulty in understanding complex content. Additionally, their performance can vary based on domain specificity. In recent times, Knowledge Graphs (KGs) have attracted considerable attention for their ability to represent structured and interconnected information, and adopted by many companies in various domains. KGs represent knowledge by depicting relationships between entities, known as facts, usually based on formal ontological models. Consequently, they enable accuracy, decisiveness, interpretability, domain-specific knowledge, and evolving knowledge in various AI applications. The intersection between GenAI and KG has ignited significant interest and innovation in Natural Language Processing (NLP). For instance, by integrating LLMs with KGs during pre-training and inference, external knowledge can be incorporated for enhancing the model’s capabilities and improving interpretability. When integrated, they offer a robust approach to problem solving in diverse areas such as information enrichment, representation learning, conversational AI, cross-domain AI transfer, bias, content generation, and semantic understanding. This workshop aims at reinforcing the relationships between Deep Learning, Knowledge Graphs, and NLP communities and foster interdisciplinary research in the area of GenAI. --------------------------------------------------------------------------------- Topics of Interest --------------------------------------------------------------------------------- * Enhancing KG construction and completion with GenAI * Multimodal KG generation * Text-to-KG using LLMs * Multilingual KGs * GenAI for KG embeddings * GenAI for Temporal KGs * Dialogue systems enhanced by KG and GenAI * Cross-domain knowledge transfer with GenAI * Bias mitigation using KGs in GenAI * Explainability with KGs and GenAI * Natural language querying of KGs via GenAI * NLP tasks using KGs and GenAI * Prompt Engineering using KGs * GenAI for Ontology learning and schema induction in KGs * Hybrid QA systems combining KGs and GenAI * Recommendation systems and KGs with GenAI * Creating benchmark datasets relevant for tasks combining KGs and GenAI * Real-world applications on scholarly data, biomedical domain, etc. * Knowledge Graph Alignment * Applying to real-world scenarios ------------------------------------------------------------------------------------ Important Dates ------------------------------------------------------------------------------------ - Submission deadline: 5 November 2024 - Notification of Acceptance: 5 December 2024 - Camera-ready paper due: 13 December 2024 - COLING2025 Workshop day: 19 January 2025 ------------------------------------------------------------------------------------ Submissions ------------------------------------------------------------------------------------ Full research papers (6-8 pages) Short research papers (4-6 pages) Position papers (2 pages) These page limits only apply to the main body of the paper. At the end of the paper (after the conclusions but before the references) papers need to include a mandatory section discussing the limitations of the work and, optionally, a section discussing ethical considerations. Papers can include unlimited pages of references and an unlimited appendix. Papers must follow the two-column format of *ACL conferences, using the official templates ( https://www.overleaf.com/latex/templates/association-for-computational-ling… <https://goto-ng.fiz-karlsruhe.de/latex/templates/association-for-computatio…> ). The templates are available for download as style files and formatting guidelines. Submissions that do not adhere to the specified styles, including paper size, font size restrictions, and margin width, will be desk-rejected. Submissions are open to all and must be anonymous, adhering to COLING 2025's double-blind submission and reproducibility guidelines. All accepted papers (after double-blind review of at least 3 experts) will appear in the workshop proceedings that will be published in ACL Anthology. At least one of the authors of the accepted papers must register for the workshop to be included into the workshop proceedings. The workshop will be a 100% in-person 1-day event at COLING 2025. Submissions must be made using the START portal: https://softconf.com/coling2025/GenAIK25/ <https://goto-ng.fiz-karlsruhe.de/coling2025/GenAIK25/,DanaInfo=softconf.com…> --------------------------------------------------------------------------------- Sponsors --------------------------------------------------------------------------------- NFDI4DataScience (NFDI4DS - https://www.nfdi4datascience.de/ <https://goto-ng.fiz-karlsruhe.de/,DanaInfo=www.nfdi4datascience.de,SSL+> ) is a national research data infrastructure for Data Science and AI project. The overarching objective of the project is the development, establishment, and sustainment of a national research data infrastructure (NFDI) for the Data Science and Artificial Intelligence community in Germany. The vision of NFDI4DS is to support all steps of the complex and interdisciplinary research data lifecycle, including collecting/creating, processing, analyzing, publishing, archiving, and reusing resources in Data Science and Artificial Intelligence. NFDI4ds is offering a total of €2000 in travel grants (€1000 each) to two selected students who will attend and present their work at GenAIK 2025! To be considered, submit your paper to the workshop, and if your paper is accepted, you’ll be eligible for a chance to receive one of the two grants. --------------------------------------------------------------------------------- Organization --------------------------------------------------------------------------------- - Genet Asefa Gesese, FIZ Karlsruhe, KIT, Germany - Harald Sack, FIZ Karlsruhe, KIT, Germany - Heiko Paulheim, University of Mannheim, Germany - Albert Meroño-Peñuela, King’s College London, UK - Lihu Chen, Imperial College London, UK If you have published in ACL conferences previously, and are interested to be part of the program committee of GenAIK2025, please fill in this form: https://forms.gle/t56dP6McD1VJmTfT9 <https://goto-ng.fiz-karlsruhe.de/,DanaInfo=forms.gle,SSL+t56dP6McD1VJmTfT9> -- *Dr.-Ing. **Genet Asefa Gesese* Head of Machine Learning Department (Abteilungsleitung Maschinelles Lernen) FIZ Karlsruhe – Leibniz Institute for Information Infrastructure ( *https://www.fiz-karlsruhe.de/en/bereiche/lebenslauf-und-publikationen-dr-ing-genet-asefa-gesese <https://www.fiz-karlsruhe.de/en/bereiche/lebenslauf-und-publikationen-dr-in…>* ) AND Karlsruhe Institute of Technology (KIT) *( https://www.aifb.kit.edu/web/Genet_Asefa_Gesese/en <https://www.aifb.kit.edu/web/Genet_Asefa_Gesese/en> )*

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SyntaxFest 2025 - First Announcement
by Dobrovoljc, Kaja 28 Oct '24

28 Oct '24

Dear colleagues, We are pleased to announce that SyntaxFest 2025 (https://syntaxfest.github.io/syntaxfest25/) will take place in Ljubljana, Slovenia, from 26 to 29 August 2025. SyntaxFest is a biennial event that brings together a series of events focusing on topics such as empirical syntax, linguistic annotation, statistical language analysis, and natural language processing. SyntaxFest 2025, organized by the University of Ljubljana, will host five events under a unified submission process and program: * TLT: 23rd Workshop on Treebanks and Linguistic Theories * DepLing: 8th International Conference on Dependency Linguistics * UDW: 8th Universal Dependencies Workshop * IWPT: 18th International Conference on Parsing Technologies * Quasy: 2nd Workshop on Quantitative Syntax In addition, the event will be co-located with the UniDive 1st Shared Task on Morphosyntactic Parsing, organized by the UniDive COST Action CA21167, on 26 August 2025. Preliminary timeline for paper submission procedure: * First call for papers: December 2024 * Submission deadline: April 2025 * Notification of acceptance: June 2025 * Conference dates: 26 to 29 August 2025 Workshop organizers / Programme chairs: TLT: * Heike Zinsmeister (University of Hamburg) * Sarah Jablotschkin (University of Hamburg) * Sandra Kübler (Indiana University) DepLing: * Eva Hajičová (Charles University, Prague) * Sylvain Kahane (Université Paris Nanterre) UDW: * Gosse Bouma (University of Groningen) * Cagri Coltekin (University of Tübingen) IWPT: * Kenji Sagae (University of California, Davis) * Stephan Oepen (University of Oslo) Quasy: * Xinying Chen (University of Ostrava) * Yaqin Wang (Guangdong University of Foreign Studies) Local Organizing Committee: * Kaja Dobrovoljc (University of Ljubljana, chair) * Špela Arhar Holdt (University of Ljubljana) * Marko Robnik Šikonja (University of Ljubljana) * Matej Klemen (University of Ljubljana) * Luka Terčon (University of Ljubljana) * Sara Kos (University of Ljubljana) We look forward to seeing you in Ljubljana! On behalf of SyntaxFest 2025 Organizing Committee, Kaja Dobrovoljc

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2nd Call for papers "WRAICogS1 - Writing Aids at the Crossroads of AI, Cognitive Science, and NLP" (COLING workshop)
by Michael Zock 27 Oct '24

27 Oct '24

(Apologies for cross-posting) Second call for papers: "WRAICogS1 - Writing Aids at the Crossroads of AI, Cognitive Science, and NLP" * Co-located with COLING 2025, Abu Dhabi, https://coling2025.org/ * SUBMISSION DEADLINE: November 25, 2024 * SUBMISSION LINK: https://softconf.com/coling2025/AAC-AI25/ KEYNOTE SPEAKER Cerstin Mahlow, Professor of Digital Linguistics and Writing Research, ZHAW School of Applied Linguistics, Winterthur, Switzerland MOTIVATION This workshop is dedicated to developing writing aids grounded in human cognition (limitations of attention and memory, typically observed habits, knowledge states, and information needs). In other words, we focus on the cognitive and engineering aspects of interactive writing. Our goal is not only to help people acquire and improve their writing skills but also to enhance their productivity. By leveraging computer technology, we aim to enable them to produce better texts in less time. Writing is one of the four cornerstones of communication. By leaving a trace, it allows us to reach many people, to transcend space and time, and to spare ourselves the trouble of memorization. Writing is undeniably important, whether as a communication tool, a thinking aid, or a memorial support. However, what is less obvious is the process—that is, the precise steps required to transform an intuition or vague idea into concrete, well-polished prose. Producing readable, well-written text requires many skills, deep and broad knowledge of various sorts (topic, language, audience, metaknowledge, i.e., how to use the information at hand?)— a lot of practice and appropriate feedback. No one can learn all this overnight. The quantity and diversity of knowledge to interiorize, as well as the variety of cognitive states encountered, may explain why writing is so difficult and why it takes time to gain control over the whole process and become an expert writer. Unfortunately, knowledge alone is not enough. Writing is also a time- and energy-consuming endeavor. It is very hard work. Since writing is difficult, and since there are now computer programs capable of doing it, one may wonder: - whether we should leave the job entirely to the machine, or - whether we could use these programs to help people write or to acquire the skill of writing. Indeed, there are situations where it makes sense to rely on machines (e.g., routine work, business letters), but there are also many situations where this strategy is not recommended (e.g., writing to understand, writing to enrich and clarify our thoughts, writing to support thinking). That being said, one may find a middle ground where humans and machines work together, each contributing their strengths. It remains to be seen where machines can assist in the process (e.g., idea generation, idea structuring, translation into language, revision, editing) and where it is better to leave control to humans. Hence, the main question is not whether we should use LLMs to produce texts, but rather how, when, and at what level to use them or other techniques to help people produce written text. In sum, our main goal is not to substitute machines for people or to have them do the job in people's place, but rather to have machines assist people. Specifically, we aim to help people learn to write, speed up the process, gain better control, and reduce stress and cognitive load. Our motivation is largely practical and educational. Obviously, we are not the first ones to pursue this goal. However, while many workshops focused on developing educational software, creating intelligent writing assistants, or evaluating written text, the submitted papers have primarily addressed formal aspects, such as grammatical error detection and spotting spelling mistakes. Yet good writing (text composition) requires much more than just the production of well-formed sentences. Our mission is to go beyond merely identifying errors or mistakes made at the very end of the writing process, such as those due to ignorance or inattention. Instead, we aim to evaluate the quality of the choices made at higher levels. In other words, we are interested in the full spectrum of writing, including technology-based writing aids that address all tasks involved in writing: conceptual planning (ideation, organization), linguistic expression, editing, and revision. Hence, we welcome papers that focus on the higher levels of composition—such as thinking, reasoning, and planning (idea generation, outline planning)—as well as those concerned with the lower levels (grammar, spelling, and punctuation). Arguably, this is the first workshop to: - Consider the entire spectrum of writing rather than only the lower levels, - Integrate humans right from the start into the development cycle of writing aids, and - Provide support and feedback at any moment —before, during, and after writing— rather than only at the very end. TOPICS We welcome contributions on all topics related to writing aids, including but not limited to the following: 1. THE HUMAN PERSPECTIVE: Cognitive scientific viewpoints, including education, psycholinguistics, and neuroscience. (a) Support: How can AI tools support critical thinking and logical reasoning in writing? How can writing assistants tailor feedback to individual writers, considering their unique needs and styles? How can we assess the quality and impact of AI-generated feedback on students' writing (methods, metrics, etc.)? (b) Topical coherence: How can we help people organize their ideas into a coherent whole? How do we model or operationalize the concept of a topic, the paragraph's most central element? How do we detect possible topics within our data? What are typical subtopics of a given topic, and how do we identify them? How do we cluster content/ideas into topics and give the clusters appropriate names? (c) Building software: How do we include humans in the development cycle of writing aids? How and at what level can engineers use insights from psycholinguistics and neuroscience? How can they model the writing process while accounting for human and technological factors? (d) Metacognition: What do people typically know about writing in general and their own writing in particular? What are their problems and needs? How do people manage to coordinate the different processes? What should an authoring ecosystem look like (components)? What could be automated, and what is best left for interactive processing? (e) Shared tasks: What kinds of shared task would be meaningful while being technically feasible? 2. THE ENGINEERING SIDE (a) LLMs: Where in the writing process could we use methods developed in AI (e.g., LLMs) or computational linguistics (e.g., content generation, content structuring, translation into language, revision)? What are the potential benefits, dangers, and limitations of LLMs as writing aids? How could revealing the 'knowledge' embedded within black-box models improve their effectiveness, particularly in terms of increasing the accuracy and relevance of the feedback they provide? How can we address challenges related to data collection, privacy, and ethical considerations in developing and deploying AI writing tools? (b) Tools and resources: What kinds of tools and resources (e.g., Sketch Engine, Rhetorical Structure Theory, knowledge graphs, and linked data) could be useful? (c) Quality assessment: How can we check the veracity of facts, relevance, cohesion, coherence, style, fluency, proper use of pronouns, grammar, word choice, spelling, and punctuation? (d) Enhancement and evaluation: How do we enhance text analysis during or after writing (e.g., quality of coherence, style) using corpus linguistic tools? How do we evaluate or compare existing writing assistants (e.g., adequacy, design features, ease of use, lessons learned)? SUBMISSION INSTRUCTIONS Please submit your papers via the START/SoftConf submission portal (https://softconf.com/coling2025/AAC-AI25/), following the COLING 2025 templates. Submitted versions must be anonymous and should not exceed 8 pages for long papers and 4 pages for short papers. References do not count toward the page limit, and may be up to 4 pages long. Supplementary material and appendices are also allowed. We also invite papers discussing tools and applications (system demonstrations) related to our workshop topics. PUBLICATION All the accepted papers (be it for oral presentation or as poster) will be published as proceedings appearing in the ACL anthology. PARTICIPATION The workshop requires a physical presence. If any authors are unable to attend and present in person, alternative arrangements (such as remote presentations or video recordings) may be considered. However, we cannot guarantee these options, as the COLING organizers and local chairs have informed us that they will not provide technical support or online access. Generally, work presented in person will be given preference over work presented virtually. ORGANIZERS * Michael Zock (CNRS, LIS, Aix-Marseille University, Marseille, France) * Kentaro Inui (Mohamed bin Zayed University of Artificial Intelligence, UAE; Tohoku University, Japan; RIKEN, Japan) * Zheng Yuan (King's College London and the University of Cambridge, UK) MORE DETAILS: * homepage : https://sites.google.com/view/wraicogs1 * better readable CFP : https://sites.google.com/view/wraicogs1/home/call-for-papers * program committee : https://sites.google.com/view/wraicogs1/home/programme-committee * background information : https://sites.google.com/view/wraicogs1/home/background-and-topics -- Michael ZOCK Emeritus Research Director CNRS LIS UMR 7020 (Group TALEP) Aix Marseille Université 163 avenue de Luminy - case 901 13288 Marseille / France Mail: michael.zock(a)lis-lab.fr <mailto:michael.zock@lis-lab.fr> Tel.: +33 (0)6 51.70.97.22 Secr.: +33 (0)4.86.09.04.60 http://pageperso.lif.univ-mrs.fr/~michael.zock/ <http://pageperso.lif.univ-mrs.fr/%7Emichael.zock/>

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