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Funded PhD Natural language processing of electronic health records to improve empirical prescribing in acute admissions
by Mark Lee 26 Jul '23

26 Jul '23

Application Deadline: 30 August 2023 Details This project has a specific focus in managing the single greatest threat to global health, the increasing burden from infections caused by bacteria that are resistant to antibiotics (antimicrobial resistance, AMR). Doctors (humans) can’t reliably know which antibiotic to administer in an emergency. In fact, based on our earlier research they get it wrong about 20% of the time. A serious bacterial infection will look the same whether the bacteria causing the infection are resistant to certain antibiotics or not, and the first antibiotic must be selected on very limited information and be given the first hour of admission to hospital if there is a risk they have developed an infection that is spreading through their body. Understandably, this ‘high stakes’ uncertainty promotes the use of ‘broad-spectrum’ antibiotics which should be held in reserve for known drug-resistant infections. Natural language processing (NLP) has the potential to safely unlock successful antimicrobial stewardship for AMR at the first dose. In earlier work, we used quantitative and categorical data from electronic health records (EHRs) from patients who needed emergency hospital admission to see which antibiotics were given in the emergency room, how often a patient was prescribed an antibiotic that their bacterial infection was resistant to (under-prescribing), and how often a broad-spectrum antibiotic was used when another antibiotic alternative would have been equally effective (over-prescribing). We trained a machine learning algorithm that was allowed to under-prescribe at the same rate as doctors (about 20% of the time), that could also reduce the use of broad-spectrum antibiotics by about 40% by anticipation of which patients were unlikely to have an AMR infection. This powerful proof-of-concept work shows the huge potential for AI in personalised medicine and antimicrobial stewardship at the first and most important dose. Taking the next steps in AI for AMR. We know that a lot of important information is held in free text clinician notes that aren’t reflected in the data we used to build the model, and want to understand what valuable information contained in the free text data would help improve prediction accuracy. This project aims to analyse free-text clinician notes to retrieve valuable information that can improve the prescribing of antibiotics by more accurately predicting an individual patient’s risk of having an antibiotic-resistant infection. We are seeking a motivated student to undertake a 4 year funded PhD, in collaboration with Shionogi, a pharmaceutical company with offices in London. Eligiblity The successful candidate will hold a bachelor’s degree (or above) in Computer Science, Physics, Mathematics, Psychology or related discipline and have proven experience in computational linguistics, natural language processing, machine learning. Previous experience of applying AI methods to the medical domain is a strong advantage. Furthermore, the candidate will have strong programming skills, expertise in machine learning approaches and be excited be the challenges of interdisciplinary research between medicine and computer science. We want our PhD student cohorts to reflect our diverse society. UoB is therefore committed to widening the diversity of our PhD student cohorts. UoB studentships are open to all and we particularly welcome applications from under-represented groups, including, but not limited to BAME, disabled and neuro-diverse candidates. We also welcome applications for part-time study. The University of Birmingham works closely with University Hospitals Birmingham NHS Foundation Trust (UHB), which is the single-largest Acute NHS Trust in the UK, and serves the healthcare needs of over 1.2m people in the second-largest city in the UK. PIONEER, the Health Data Research Hub for Acute Care, alone includes >1.2m patient episodes per year with >10yrs longitudinal health data. This experienced collaboration means we are uniquely positioned to develop, model and then later embed AI-supported antimicrobial stewardship within a clinical trial and electronic prescribing systems. The student will be located at the Institute of Microbiology and Infection (IMI) of the University of Birmingham, the largest academic research institute in the field of microbiology and infectious diseases in the United Kingdom. The IMI is part of the School of Medical and Dental Sciences, defining the future of health and medicine through the provision of innovative education and exceptional research. Throughout the PhD project, regular meetings with industry partner colleagues at Shionogi will be held to monitor progression and support the student in their research. About Shionogi Established in Japan 140 years ago, Shionogi has a history of drug discovery and scientific rigour in addressing some of the toughest challenges in healthcare. Shionogi’s work in antimicrobial resistance (AMR) is a key part of our contribution to the UN Sustainable Development Goals (SDGs) - we invest the highest proportion of our pharmaceutical revenues in relevant anti-infectives R&D compared to other large pharmaceutical companies. Shionogi announced the first-ever licence agreement for an antibiotic to treat serious bacterial infections between a pharmaceutical company and a non-profit organisation driven by public health priorities. Working with the Global Antibiotic Research and Development Partnership (GARDP) and the Clinton Health Access Initiative (CHAI), the agreement aims to provide 135 countries with access. At Shionogi, our belief is that sustainable growth hinges not only on new drug creation, but also on consolidating our strengths in areas of strategic focus. Through external partnerships, we seek to bring benefits to more patients through collaboration in areas where it would be difficult for us to go it alone. Globally, the number of our partners, including partnerships across a range of industries, including academia, enables us to accelerate innovation to better help societies manage some of the most important public health threats and to take on areas where the unmet clinical need is greatest. Funding Notes The position offered is for three and a half years full-time study. The current (2023-24) value of the award is stipend; £18,622 pa; tuition fee: £4,712 pa. Awards are usually incremented on 1 October each following year. The package includes a Macbook Air and funding for additional training and conference attendance. References Moran E, Robinson E, Green C, Keeling M, Collyer B. Towards personalized guidelines: using machine-learning algorithms to guide antimicrobial selection. J Antimicrob Chemother. 2020. doi:10.1093/jac/dkaa222 Cavallaro M, Moran E, Collyer B, McCarthy ND, Green C, Keeling MJ. Informing antimicrobial stewardship with explainable AI. bioRxiv. 2022. doi:10.1101/2022.08.12.22278678 https://www.findaphd.com/phds/project/natural-language-processing-of-electr… With best regards, Mark Lee Professor of Artificial Intelligence School of Computer Science University of Birmingham www.cs.bham.ac.uk/~mgl<http://www.cs.bham.ac.uk/~mgl>

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HASOC 2023 tasks - Call for Participation - Hate Speech and Offensive Content Identification
by Thomas Mandl 26 Jul '23

26 Jul '23

15th meeting of /Forum for Information Retrieval Evaluation* HASOC-2023*/ We are excited to announce the 5th edition of HASOC, consisting of four interesting shared tasks. We invite you to participate. *Task 1 focus on identifying hate speech, offensive language, and profanity in different languages using natural language processing techniques.* * Task 1A deals with identifying hate and offensive content in Sinhala, a low-resource Indo-Aryan language spoken in Sri Lanka. The task involves classifying tweets into Hate and Offensive (HOF) or Non-Hate and Offensive (NOT). The dataset for this task is based on the Sinhala Offensive Language Detection dataset. * Task 1B focuses on identifying hate and offensive content in Gujarati, another low-resource Indo-Aryan language spoken by approximately 50 million people in India. Similarly, participants need to classify tweets into HOF or NOT categories. The training set for this task consists of around 200 tweets. For more details, please visit task 1 page <https://hasocfire.github.io/hasoc/2023/task1.html>. *Task 2, Identification of Conversational Hate-Speech in Code-Mixed Languages (ICHCL), addresses the challenge of identifying hate speech and offensive content in code-mixed conversations on social media. Code-mixed text includes multiple languages within a single conversation. The task is divided into two subtasks.* * In Task 2a, participants need to perform binary classification on conversational tweets with tree-structured data. They must determine whether a tweet, comment, or reply contains hate speech, offensive language, or profanity (HOF) or is non-hate and offensive (NOT). The classification should consider both the individual content and support for hate expressed in the parent tweet. * Task 2b involves the classification of conversational tweets with tree-structured data into specific forms of hate. Participants must identify if the tweet, comment, or reply contains standalone hate (SHOF), contextual hate (CHOF) that supports hate expressed in the parent, or if it is non-hate (NONE). For more details, please visit Task 2 webpage. <https://hasocfire.github.io/hasoc/2023/ichcl.html> *Task 3 aims to detect hateful spans within a sentence already considered hateful. A hate span is a set of continuous tokens that, in tandem, communicate the explicit hatefulness in a sentence.* * For instance, in the statement, "Women ... Can't live with them... Can't shoot them," the portion highlighted in bold will be considered a hateful span. This shared task aims to extract all such spans from a hateful text. * The input texts are all in English. The detection of hateful spans is achieved by mapping this into a sequence labeling problem. For every token of the sequences, we have manually annotated the start and end of a hateful span. This is achieved by the BIO notation tagging, where B' represents the beginning of the hate span,' I' forms the continuation of a hate span, and' O' represents the non-hate tag. The task is then to learn the correct sequence of the BIO tags for a given sentence. For example, in the above sentence, the tag sequence for the preprocessed sentence will be of the form "women can't live with them can't shoot them" → "O O O O O B I I"; "I" notation cannot exist on its own and will always be preceded by either an "I" or "B". Consequently, a “B” notation can be immediately followed by an “O” in case the span is just a single word. For more details, please visit Task 3 webpage. <https://lcs2.in/hatenorm-2023/> *Task 4 aims to detect hate speech in Bengali, Bodo, and Assamese languages. It is a binary classification task. Each dataset (for the three languages) consists of a list of sentences with their corresponding class (hate or offensive (HOF) or not hate (NOT)). Data is primarily collected from Twitter, Facebook, and Youtube comments. * The Macro F1 score will be the yardstick of the task. Team rank will be determined based on the Macro F1 score of the first part. For more details, please visit Task 4 webpage. <https://sites.google.com/view/hasoc-2023-annihilate-hates/home> Registration for all four tasks is open on our registration page. <https://hasocfire.github.io/hasoc/2023/registration.html> We believe that your expertise and contribution will be invaluable in advancing the state-of-the-art hate speech classification. We encourage you to participate in this exciting shared task and contribute to the research community. Regards, HASOC organizing team

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NLP post at City, University of London
by angus.roberts＠kcl.ac.uk 26 Jul '23

26 Jul '23

Hi everyone City, University of London are looking for postdoc NLP research fellow to work on the VISION - Violence, Health and Society project, to develop methods for extraction of information on violence from public sector records. Details below. Please circulate to anyone you think might be interested. https://www.city.ac.uk/about/jobs/apply/details.html?nPostingId=1579&nPosti… best wishes -- Angus

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CFP - SIMBig 2023 in Mexico - Extended Deadline
by Juan Antonio Lossio-Ventura 26 Jul '23

26 Jul '23

> [Apologies for cross-posting] > ====================================================================== > EXTENDED DEADLINE TO **JULY 31** > ====================================================================== > > SIMBig 2023 - 10th International Conference on Information Management and Big Data > Where: Instituto Politécnico Nacional, Mexico DF, MEXICO > When: October 18 - 20, 2023 > Website: https://simbig.org/SIMBig2023/ > > ====================================================================== > > OVERVIEW > ---------------------------------- > > SIMBig 2023 seeks to present new methods of Artificial Intelligence (AI), Data Science, Machine Learning, Natural Language Processing, Semantic Web, and related fields, for analyzing, managing, and extracting insights and patterns from large volumes of data. > > > KEYNOTE SPEAKERS (to be confirmed) > ---------------------------------- > Mona Diab, Meta AI, USA Huan Liu, Arizona State University, USA > > and more to be announced soon... > > IMPORTANT DATES > ---------------------------------- > > July 24, 2023 July 31, 2023 --> Full papers and short papers due > August 28, 2023 --> Notification of acceptance > September 10, 2023 --> Camera-ready versions > October 18 - 20, 2023 --> Conference held in Mexico DF, Mexico > > PUBLICATION > ---------------------------------- > > All accepted papers of SIMBig 2023 (tracks including) will be published with Springer CCIS Series <https://www.springer.com/series/7899> (to be confirmed). > > Best papers of SIMBig 2023 (tracks including) will be selected to submit an extension to be published in the Springer SN Computer Science Journal. <https://www.springer.com/journal/42979> > TOPICS OF INTEREST > ---------------------------------- > > SIMBig 2023 has a broad scope. We invite contributions on theory and practice, including but not limited to the following technical areas: > > Artificial Intelligence > Big/Masive Data > Data Science > Machine Learning > Deep Learning > Natural Language Processing > Semantic Web > Data-driven Software Engineering > Data-driven software adaptation > 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 > Crowdsourcing > > SPECIAL TRACKS > ---------------------------------- > > SIMBig 2023 proposes six special tracks in addition to the main conference: > > ANLP <https://simbig.org/SIMBig2023/en/anlp.html> - Applied Natural Language Processing > DISE <https://simbig.org/SIMBig2023/en/dise.html> - Data-Driven Software Engineering > EE-AI-HPC <https://simbig.org/SIMBig2023/en/eeaihpc.html> - Efficiency Enhancement for AI and High-Performance Computing > SNMAM <https://simbig.org/SIMBig2023/en/snmam.html> - Social Network and Media Analysis and Mining > > CONTACT > ---------------------------------- > > SIMBig 2023 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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HASOC 2023 tasks - Call for Participation - Hate Speech and Offensive Content Identification
by Dola Mullage, Damith P. 25 Jul '23

25 Jul '23

15th meeting of Forum for Information Retrieval Evaluation HASOC-2023 We are excited to announce the 5th edition of HASOC, consisting of four interesting shared tasks. We invite you to participate. Task 1 focuses on identifying hate speech, offensive language, and profanity in different languages using natural language processing techniques. * Task 1A is identifying hate and offensive content in Sinhala, a low-resource Indo-Aryan language spoken mainly in Sri Lanka. The task involves classifying tweets into Hate and Offensive (HOF) or Non-Hate and Offensive (NOT). The training set for this task is based on the Sinhala Offensive Language Detection dataset, which contains 10,000 tweets. * Task 1B focuses on identifying hate and offensive content in Gujarati, another low-resource Indo-Aryan language spoken by approximately 50 million people in India. Similarly, participants need to classify tweets into HOF or NOT categories. The training set for this task consists of around 200 tweets. For more details, please visit task 1 page<https://url6.mailanyone.net/scanner?m=1qOOEH-000CON-4S&d=4%7Cmail%2F14%2F16…>. Task 2, Identification of Conversational Hate-Speech in Code-Mixed Languages (ICHCL), addresses the challenge of identifying hate speech and offensive content in code-mixed conversations on social media. Code-mixed text includes multiple languages within a single conversation. The task is divided into two subtasks. * In Task 2a, participants need to perform binary classification on conversational tweets with tree-structured data. They must determine whether a tweet, comment, or reply contains hate speech, offensive language, or profanity (HOF) or is non-hate and offensive (NOT). The classification should consider both the individual content and support for hate expressed in the parent tweet. * Task 2b involves the classification of conversational tweets with tree-structured data into specific forms of hate. Participants must identify if the tweet, comment, or reply contains standalone hate (SHOF), contextual hate (CHOF) that supports hate expressed in the parent, or if it is non-hate (NONE). For more details, please visit Task 2 webpage.<https://url6.mailanyone.net/scanner?m=1qOOEH-000CON-4S&d=4%7Cmail%2F14%2F16…> Task 3 aims to detect hateful spans within a sentence already considered hateful. A hate span is a set of continuous tokens that, in tandem, communicate the explicit hatefulness in a sentence. * For instance, in the statement, "Women ... Can't live with them... Can't shoot them," the portion highlighted in bold will be considered a hateful span. This shared task aims to extract all such spans from a hateful text. * The input texts are all in English. The detection of hateful spans is achieved by mapping this into a sequence labeling problem. For every token of the sequences, we have manually annotated the start and end of a hateful span. This is achieved by the BIO notation tagging, where B' represents the beginning of the hate span,' I' forms the continuation of a hate span, and' O' represents the non-hate tag. The task is then to learn the correct sequence of the BIO tags for a given sentence. For example, in the above sentence, the tag sequence for the preprocessed sentence will be of the form "women can't live with them can't shoot them" → "O O O O O B I I"; "I" notation cannot exist on its own and will always be preceded by either an "I" or "B". Consequently, a “B” notation can be immediately followed by an “O” in case the span is just a single word. For more details, please visit Task 3 webpage.<https://url6.mailanyone.net/scanner?m=1qOOEH-000CON-4S&d=4%7Cmail%2F14%2F16…> Task 4 aims to detect hate speech in Bengali, Bodo, and Assamese languages. It is a binary classification task. Each dataset (for the three languages) consists of a list of sentences with their corresponding class (hate or offensive (HOF) or not hate (NOT)). Data is primarily collected from Twitter, Facebook, and Youtube comments. The Macro F1 score will be the yardstick of the task. Team rank will be determined based on the Macro F1 score of the first part. For more details, please visit Task 4 webpage.<https://url6.mailanyone.net/scanner?m=1qOOEH-000CON-4S&d=4%7Cmail%2F14%2F16…> Registration for all four tasks is open on our registration page.<https://url6.mailanyone.net/scanner?m=1qOOEH-000CON-4S&d=4%7Cmail%2F14%2F16…> We believe that your expertise and contribution will be invaluable in advancing the state-of-the-art hate speech classification. We encourage you to participate in this exciting shared task and contribute to the research community. Regards, HASOC organizing team

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Call For Participation: Machine Translation for Indian Languages (MTIL)
by Surupendu Gangopadhyay 25 Jul '23

25 Jul '23

Apologies for multiple posting *********************************** *------------------------------------------------------------------------------------------------Machine Translation for Indian Languages (MTIL) 2023------------------------------------------------------------------------------------------------* We invite all IR and NLP researchers and enthusiasts to participate in the MTIL track (https://mtilfire.github.io/mtil/2023/) held in conjunction with the Forum for Information Retrieval Evaluation (FIRE) 2023 ( http://fire.irsi.res.in/). Indian languages have many linguistic complexities. Though some Indian languages share syntactic similarities, some possess intricate morphological structures. At the same time, some Indian languages are low-resource. Therefore the machine translation models should address these unique challenges in translating between Indian languages. The MTIL track consists of two tasks: 1. *General Translation Task (Task 1):* Task participants should build a machine translation model to translate sentences of the following language pairs: 1. Hindi-Gujarati 2. Hindi-Kannada 3. Kannada-Hindi 4. Hindi-Odia 5. Odia-Hindi 6. Hindi-Punjabi 7. Punjabi-Hindi 8. Hindi-Sindhi 9. Urdu-Kashmiri 10. Telugu-Hindi 11. Hindi-Telugu 12. Urdu-Hindi 13. Hindi-Urdu 2. *Domain Specific Translation Task (Task 2)*: Task participants will build machine translation models for Governance and Healthcare domains. 1. Healthcare: a. Hindi-Gujarati b. Kannada-Hindi c. Hindi-Odia d. Odia-Hindi e. Hindi-Punjabi f. Kannada-Hindi 2. Governance: a. Hindi-Gujarati b. Kannada-Hindi c. Hindi-Odia d. Odia-Hindi e. Hindi-Punjabi f. Kannada-Hindi *Dataset:* The primary source of parallel language pairs is Bharat Parallel Corpus Collection (BPCC), released by AI4Bharat (https://ai4bharat.iitm.ac.in/bpcc ). Participants are encouraged to add datasets of their choice, including parallel corpora and monolingual datasets, to train their models. More information on registration and participation in the track can be found here: https://mtilfire.github.io/mtil/2023/ This track is being done in association with BHASHINI ( https://bhashini.gov.in/) *Organisers* - Prasenjit Majumder, DAIICT Gandhinagar,India and TCG CREST, Kolkata,India - Arafat Ahsan, IIIT-Hyderabad,India - Asif Ekbal, IIT-Patna,India - Saran Pandian, DAIICT Gandhinagar,India - Ramakrishna Appicharla, IIT-Patna ,India - Surupendu Gangopadhyay, DAIICT Gandhinagar,India - Ganesh Epili, DAIICT Gandhinagar,India - Dreamy Pujara, DAIICT Gandhinagar,India - Misha Patel, DAIICT Gandhinagar,India - Aayushi Patel, DAIICT Gandhinagar,India - Bhargav Dave, DAIICT Gandhinagar,India - Mukesh Jha, DAIICT Gandhinagar,India

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2-years Postdoc position in NLP at Bocconi University (Italy)
by debora.nozza＠unibocconi.it 24 Jul '23

24 Jul '23

We are seeking a highly motivated and talented individual to join our research team as a Postdoctoral Researcher in the field of Natural Language Processing. The position offers an exciting opportunity to investigate the computational and algorithmic aspects underlying modern Artificial Intelligence systems, with a specific focus on the algorithmic and application aspects of NLP-based technologies. Successful candidates will work closely with Prof. Debora Nozza, Prof. Dirk Hovy, and the MilaNLP lab. Your profile: - a Ph.D. in Computer Science, Computational Linguistics/NLP, Machine Learning, Data Science, or related fields. - Excellent programming skills in Python. - Fluency in spoken and written English. Knowledge of Italian is NOT a requirement. - Knowledge of current neural network models and implementation tools for neural networks (e.g., PyTorch). - Experience with publications in top-tier venues in the field of NLP/Computational Linguistics. Position Details: - Starting date: Oct 1 2023, or any time thereafter - Duration: 2 years - Deadline: 1st September 2023 - Competitive Salary: Applicants from outside Italy may qualify for a researcher taxation scheme How to apply: Go to the Bocconi postdoc job market page https://jobmarket.unibocconi.eu/?type=a&urlBack=/wps/wcm/connect/Bocconi/Si… and search for “Natural language processing”, where you can also find the official job description. Candidates should attach publications and a cover letter to their application. Online interviews will take place during September 2023. Please contact debora.nozza(at)unibocconi.it if you have any questions.

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Post doc Position in Corpus Linguistics or NLP at TurkuNLP
by Veronika Laippala 24 Jul '23

24 Jul '23

Dear colleagues, Our research group TurkuNLP at the University of Turku, Finland, has an opening for *a post doc position in corpus linguistics or NLP.*. The position is part of the research project "Massively Multilingual Modeling of Registers in Web-Scale Data," (MMMReg) which is funded by the Academy of Finland. The project aims to explore language use in the digital world at a massively multilingual scale using neural networks. The specific focus of the project is on web registers, such as news, blogs, and how-to pages. The primary goals of the project are to analyze the linguistic characteristics of web registers across languages and to develop machine learning methods for modeling registers in large web datasets at a massively multilingual scale. The position is for one year, starting on September 1, 2023. The closing date for the applications is August 7, 2023 (UTC+3) For more information on the position, please visit https://www.utu.fi/en/university/come-work-with-us/open-vacancies Do not hesitate to get in touch if you have any questions! Best regards, Veronika Laippala TurkuNLP, University of Turku, Finland

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3rd Workshop DL4LD: Addressing Deep Learning, Relation Extraction, and Linguistic Data
by Radovan Garabik 23 Jul '23

23 Jul '23

Third Workshop DL4LD 2023 Deep learning for linguistic linked data: Addressing Deep Learning, Relation Extraction, and Linguistic Data with a Case Study on The Bigger Analogy Test Set (BATS) https://vecto.space/projects/BATS/ Venue: Vienna, Austria, University of Vienna & online Website: http://dl4ld2023.mruni.eu/ Date: 13 September 2023 The Cost Action CA18209 NexusLinguarum ( https://nexuslinguarum.eu ) invites you to attend the Third Workshop Deep Learning for Linguistic Linked Data: Addressing Deep Learning, Relation Extraction, and Linguistic Data with a Case Study on The Bigger Analogy Test Set (BATS) – DL4LD 2023, organized as part of LDK 2023 ( http://2023.ldk-conf.org ). We are glad to announce that the program features one keynote, Assistant Professor Michael Cochez, and seven oral presentations. DL4LD 2023 will be a hybrid event (in-person and online) open to anyone interested in the topic. Online participation is still possible but requires prior registration. The registration form ( https://ldk-registration.univie.ac.at ) for online participation will be open until 3rd September 2023. We are very much looking forward to seeing you in Vienna or online. Program available here: http://dl4ld2023.mruni.eu/?page_id=323

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Joint Call for Tutorial Proposals - EACL/NAACL/ACL/EMNLP 2024
by Yuval Pinter 23 Jul '23

23 Jul '23

The European Chapter of the ACL (EACL), the North American Chapter of the ACL - Human Language Technologies (NAACL-HLT), the Association for Computational Linguistics (ACL), and the Conference on Empirical Methods in Natural Language Processing (EMNLP) invite proposals for tutorials in conjunction with EACL 2024, NAACL-HLT 2024, ACL 2024, and EMNLP 2024. We seek proposals in all areas of computational linguistics, broadly conceived to include related disciplines. We invite proposals for two types of tutorials: Cutting-edge: tutorials that cover advances in newly emerging areas not previously covered in any EACL/NAACL-HLT/ACL/EMNLP related tutorial (see the list of tutorials in the past 4 years). Introductory to computational linguistics/NLP: tutorials that provide introductions to related fields that are potentially relevant for the computational linguistics community (e.g., linguistics, bioinformatics, machine learning techniques, large language models for Non-English languages). In both cases, the aim of a tutorial is primarily to help understand a scientific problem, its tractability, and its theoretical and practical implications. Presentations of particular technological solutions or systems are welcome, provided that they serve as illustrations of broader scientific considerations. Tutorials will be held at one of the following conference venues: * EACL 2024 in Malta on March 17-22, 2024 * NAACL-HLT 2024 in Mexico City on June 16-21, 2024 * ACL 2024 in Bangkok on August 12-17th, 2024 * EMNLP 2024 in location TBD, late in 2024 Important Dates EACL/NAACL-HLT/ACL/EMNLP 2024 shared dates: Proposal submission deadline: September 1, 2023 Notification of acceptance: October 1, 2023 All deadlines are 11:59PM UTC-12:00 (“anywhere on Earth”). Fee Waiving Up to 3 instructors per tutorial can have their registration fees waived for the main conference and any subset of co-located tutorials and workshops. Diversity And Inclusion To foster a really inclusive culture in our field, we particularly encourage submissions from members of underrepresented groups in computational linguistics, i.e., researchers from any demographic or geographic minority, researchers with disabilities, among others. The overall diversity of the tutorial organizers and potential audience will be taken into account to ensure that the conference program is varied and balanced. Tutorial proposals should describe and will be evaluated according to how the tutorial contributes to topics promoting diversity (e.g., working on minority languages or groups), participation diversity (e.g., coordinating with social affinity groups, providing subsidies, making a promotional plan for the tutorial), and representation diversity among tutorial presenters. For more information or advice, organizers may consult resources such as the BIG directory, Black in AI, Disability in AI, Indigenous AI, LatinX in AI, Masakhane, 500 Queer Scientists, and Women-in-ML’s directory. Submission Details Proposals should use the ACL paper submission format. Authors can download the LaTeX or Word template or use the Overleaf template. Proposals should not exceed 4 pages of content (plus unlimited pages for references), should be submitted as PDF documents, and should contain the following: * A title and authors, affiliations, and contact information. * A brief description of the tutorial content and its relevance to the computational linguistics community. * Type of the tutorial: cutting-edge vs introductory. * Briefly describe the target audience and any expected prerequisite background the audience should be aware of. Specification of any prerequisites for the attendees. Here are some examples: ** Math: e.g., “Understand derivatives and integrals as found in introductory calculus” ** Linguistics: e.g., “Be able to parse and generate text with dependency grammars” ** Machine Learning: e.g., “Understand ‘classical’ supervised methods such as SVM and perceptron” ** Other areas: e.g., “Familiarity with word2vec” ** Programming or other tools: e.g., “Knowledge of Pytorch and Unix command line tools” * An outline of the tutorial structure content and how it will be covered in a three-hour slot. In exceptional cases six-hour tutorial slots are available. These time limits do not include coffee breaks, e.g., a three-hour tutorial in fact occupies a 3.5-hour slot, and a six-hour tutorial occupies a 7-hour slot. * Explain how the tutorial includes other people’s work. We recommend that the tutorial covers work by the presenters as well as by other researchers. The submission should explain how this breadth is ensured. Tutorials should not be “self-invited talks”. * Diversity considerations, e.g., use of multilingual data, indications of how the described methods scale up to various languages or domains, participation of both senior and junior instructors, demographic and geographical diversity of the instructors, plans for how to diversify audience participation, etc. * Reading list. Work that you expect the audience to read before the tutorial can be indicated by an asterisk. Recommended papers should provide breadth of authorship and include work by other authors, and work from other disciplines is welcome if relevant. * For each tutorial presenter, a one-paragraph statement of their research interests and areas of expertise for the tutorial topic, as well as experience in instructing an international audience. * An estimate of the audience size for the tutorial. If the same or a similar tutorial has been given before, include information on where any previous version of the tutorial was given and how many attendees the tutorial attracted. * A note specifying which venue(s) (EACL/NAACL-HLT/ACL/EMNLP) would be acceptable and/or preferable. Include a description of any constraints that might make the tutorial compatible with only one of these events, logistically, thematically, or otherwise. * A description of special requirements for technical equipment. * We intend to make tutorial presentation materials publically available (e.g., tutorial slides, captioned video recording, as well as software, data, or other resources as applicable) in the ACL Anthology. If any of your tutorial materials cannot be shared, please explain. * An ethics statement that discusses ethical considerations related to the topics of the tutorial. * OPTIONAL: We welcome proposals on special conference themes. If your tutorial proposal aligns with the special theme of a conference, then please explain. * OPTIONAL: We invite tutorial instructors to include pedagogical material that the audience can bring into classrooms or similar spaces of discussion, to bring attention to the tutorial topic (e.g., a hands-on exercise, discussion questions, a demo, or an assignment). If you would like to provide this, then please explain. Tutorial proposals should be submitted online using the softconf system at the following link: https://softconf.com/n/acl-tutorials2024. Proposals will be reviewed jointly by the Tutorial Co-Chairs of the conferences and by a group of external experts. Evaluation Criteria Each tutorial proposal will be evaluated according to its clarity and preparedness, novelty or timely character of the topic, instructors’ experience, likely audience interest, open access of the tutorial instructional material, and diversity and inclusion. Instructor Responsibilities Accepted tutorial presenters will be notified by October 1st, 2023. They must then provide abstracts of their tutorials for inclusion in the conference registration material by the specific conference deadlines. The description should be in two formats: (a) an ASCII version that can be included in email announcements and published on the conference website, and (b) a PDF version for inclusion in the electronic proceedings (detailed instructions will be provided). Tutorial speakers must provide tutorial materials (e.g., slides, relevant list of papers) at least one month prior to the date of the tutorial conditioned on the final venue. The final submitted tutorial materials must minimally include copies of the course slides and a bibliography for the material covered in the tutorial. After the conference, the presenters will be invited to update their slides in the ACL Anthology (if needed). Tutorial Chairs EACL Sharid Loáiciga <sharid.loaiciga(a)gu.se> Mohsen Mesgar <mohsen.mesgar(a)bosch.com> NAACL-HLT Rui Zhang <rmz5227(a)psu.edu> Nathan Schneider <nathan.schneider(a)georgetown.edu> Snigdha Chaturvedi <snigdha(a)cs.unc.edu> ACL Luis Chiruzzo <luis.chiruzzo(a)gmail.com> Hung-yi Lee <tlkagkb93901106(a)gmail.com> Leonardo Ribeiro <leonardofribeiro(a)gmail.com> EMNLP TBA

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