Call for Participation

Introduction

Due to the fast growth of the number of scientific publications, keeping abreast of new findings and recognizing unsolved challenges are becoming more difficult for researchers in various fields. This problem could be more severe when there is a high demand in a specific topic such as the recent COVID-19 global pandemic. As such, it is necessary to be equipped with state-of-the-art technologies to effectively combine precious findings from diverse scientific documents into a single easily accessible resource. Due to the importance of this requirement for the scientific community, there have been some efforts to achieve this goal. For instance, the recent COVID-19 Open Research Dataset (CORD-19) has been published to answer questions about recent advancements on COVID-19 research including the progress on vaccine and therapeutics, risk factors, virus genetics and diagnostics. Moreover, new resources and tools released for scientific document processing such as SciBERT (Beltagy, Lo, and Cohan, 2019) provide more research opportunities for scientific document understanding. However, despite all of the recent progress, the fragmented research focusing on different aspects for this domain necessitates a forum for researchers from different perspectives to discuss achievements, new challenges, new resource requirements, and impacts of scientific document understanding on various fields. In addition to the recent focus on scholarly text processing and document understanding in natural language processing, this workshop extends SDU to other scientific areas, including but not limited to scientific image processing, automatic programming, knowledge graph manipulation, and data management. We hope that this workshop will foster the collaborations from researchers working on different scientific and AI areas for SDU. Finally, we present a shared task of acronym identification and disambiguation on scientific documents to boost the research in this area.

Topics of Interest

SDU is a workshop to gather insights into the recent advances and remaining challenges on scientific document understanding. As this topic is inherently a multi-disciplinary subject, researchers from artificial intelligence, natural language processing, information retrieval and extraction, image processing, data mining, statistics, bio-medicine, cybersecurity, finance and other fields are invited to submit papers on the recent advances, resources, tools, and upcoming challenges for SDU. Topics of interest for this workshop include but are not limited to:

  • Information extraction and information retrieval for scientific documents;

  • Question answering and question generation for scholarly documents;

  • Word sense disambiguation, acronym identification and expansion, and definition extraction;

  • Document summarization, text mining, document topic classification, and machine reading comprehension for scientific documents;

  • Graph analysis applications including knowledge graph construction and representation, graph reasoning and query knowledge graphs;

  • Multi-modal and multi-lingual scholarly text processing;

  • Biomedical image processing, scientific image plagiarism detection and data visualization;

  • Code/Pseudo-code generation from text and image/diagram captioning;

  • New language understanding resources such as new syntactic/semantic parsers, language models or techniques to encode scholarly text;

  • Survey or analysis papers on scientific document understanding and new tasks and challenges related to each scientific domain;

  • Factuality, data verification and anti-science detection;

Important Dates

  • Paper submission deadline: November 20 2020 November 25, 2020

  • Notification date: November 30, 2020 December 7, 2020

  • Camera-ready submissions due: December 18, 2020 December 25, 2020

  • SDU workshop at AAAI 2021: February 9, 2021

All deadlines are “anywhere on earth” (UTC-12)

Submission

Authors are invited to submit their unpublished work that represents novel research. The papers should be written in English using the AAAI-20 author kit (Note that the 2021 author kit should not be used, as it has a permanent AAAI copyright slug, which is not appropriate for workshops) and follow the AAAI 2020 formatting guidelines. Authors can also submit the supplementary materials, including technical appendices, source codes, datasets, and multimedia appendices. All submissions, including the main paper and its supplementary materials, should be fully anonymized. For more information on formatting and anonymity guidelines, please refer to AAAI 2021 call for paper page.

All papers will be double blind peer reviewed. SDU accepts papers in two tracks:

  • Short Paper Track: Up to 4 pages of content including the references. Upon the acceptance, the authors are provided with 1 more page to address the reviewer comments.

  • Long Paper Track: Up to 8 pages of content including the references. Upon the acceptance, the authors are provided with 1 more page to address the reviewer comments.

Two reviewers with the same technical expertise will review each paper. Authors of the accepted papers will present their work in either the Oral or Poster session. All accepted papers will appear on the workshop proceedings that will be published on CEUR-WS. The authors will keep the copyright of their papers that are published on CEUR-WS. The workshop proceedings will be indexed by DBLP.

Submission should be done electronically in PDF format via EasyChair. SDU will not accept any submission from other mechanisms such as Email. For information on System Paper submission for the share tasks, please refer to our Shared Task page.


References

Iz Beltagy, Kyle Lo, and Arman Cohan. 2019. SciBERT: A Pretrained Language Model for Scientific Text. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP).