MATCHING WORKSHOP @ ACL 2023 https://megagon.ai/matching-2023/
*I**mportant Dates* direct submission via OpenReview: April 24, 2023 submission via ARR - commitment deadline: May 8th, 2023 Notification of Acceptance: May 22, 2023 Camera-ready papers due: June 6, 2023 Pre-recorded video due: June 12, 2023 MATCHING Workshop @ACL2023 (Toronto and remote): July 13 Matching Entities from structured and unstructured sources is an important task in many domains and applications such as HR and E-commerce. For example, in HR platforms/services, it is important to match resumes to job descriptions and job seekers to companies. Similarly in web platforms/services, it is important to match customers to businesses such as hotels and restaurant, among others. In such domains, it is also relevant to match “textual customer reviews” to customers queries, and sentences (or phrases) as answers to customer questions. Recent advances in Natural Language Processing, Natural Language Understanding, Conversational AI, Language Generation, Machine Learning, Deep Learning, Data Management, Information Extraction, Knowledge Bases/Graphs, (MultiSingle Hop/Commonsense) Inference/Reasoning, Recommendation Systems, and others, have demonstrated promising results in different Matching tasks related (but not limited) to the previously mentioned domains. We believe that there is tremendous opportunity to further exploit and explore the use of advanced NLP (and language related) techniques applied to Matching tasks. Therefore, the goal of this workshop is to bring together the research communities (from academia and industry) of these related areas, that are interested in the development and the application of novel natural-language-based approaches/models/systems to address challenges around different Matching tasks. *Confirmed Invited Speakers* Alan Ritter - Georgia Tech Ndapa Nakashole - UC San Diego William Cohen - Google We invite submissions of long and short papers on original and unpublished work that address challenges around the specific task of matching information from heterogeneous sources spanning structured (e.g., databases, resume) and unstructured (e.g., online reviews, job advertisements, social media posts) data. The topics include but are not limited to: * Entity Alignment; * Entity Matching; * Entity Linking; * Language-Model-based Matching; * Data-driven Matching; * Knowledge-graph-based matching; * Rule-based Matching; * Temporal Information Matching; * Human-Centric AI for Matching; * Trust, Explainability, and Fairness in Matching; * Information extraction from user-generated text; * Robustness to noise * Matching applications: recommendations, question answering, fact checking, news and social media content identification/suggestion, etc. All machine learning, text mining, and natural language processing techniques are welcome. All regular papers and short papers should follow the ACL 2023 style guidelines: “Both long and short papers must follow the ACL 2023 two-column format, using the supplied official style files. The templates can be downloaded in Style Files and Formatting. Please do not modify these style files, nor should you use templates designed for other conferences. Submissions that do not conform to the required styles, including paper size, margin width, and font size restrictions, will be rejected without review.” The maximum length of a regular paper is 8 pages plus an unlimited number of pages for references. The maximum length of a short paper is 4 pages plus an unlimited number of pages for references. At least one author of every accepted paper is expected to attend the workshop. MATCHING is using a hybrid submission process, Authors can submit their papers using the OpenReview platform ( https://openreview.net/group?id=aclweb.org/ACL/2023/Workshop/MATCHING&referrer=%5BHomepage%5D(%2F)). Alternatively, authors can also commit papers and reviews from ARR. We allow parallel commitment to the EACL 2023 conference and our workshop, with the requirement that if the paper is accepted at EACL 2023, it will be withdrawn from archival publication at the workshop. We ask the authors to notify MATCHING if they have also committed to EACL or other workshops. All accepted short and long papers must be presented as a talk/poster/demo at the workshop, depending on the workshop schedule. At least one author of each accepted paper must register for ACL 2023 and attend the workshop. Mentorship Program We will be hosting a mentorship program to facilitate exchange between authors and experts working in areas relevant to the workshop. The goal of this program is to foster collaborations and increase the quality and impact of the submissions. Mentors are expected to guide mentees during the mentorship period as they prepare submissions for this workshop. Their interactions may include an in-depth discussion of related work to ensure submissions are well contextualized, discussions on writing and presentation of the submission, and discussions about possible extensions of the submissions. Mentees are expected to prepare the submissions by February 28, 2023 and contact their assigned mentor. Mentors and mentees are encouraged to dedicate at least 4hrs over the course of the program to maximize the benefits of the program. They can meet virtually within the first week after the mentor-mentee matching is made and set expectations for subsequent meetings. Their efforts should culminate in a final version of the paper that should be submitted by the deadline. Applications to the mentorship program are due February 28, 2023 Application to be a mentor/mentee: Apply Here: *Organizers:* Estevam Hruschka - Megagon Labs Tom Mitchell - Carnegie Mellon University Dunia Mladenić - Jozef Stefan Institute (JSI) Marko Grobelnik - Jozef Stefan Institute (JSI) Sajjadur Rahman - Megagon Labs *Contact* If you have any questions or inquiries regarding the workshop or need further information, please do not hesitate to send an email to [email protected] -- Estevam Hruschka Lab Director and Staff Research Scientist Megagon Labs - www.megagon.ai -- Sent from my iPhone
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