Dear colleagues, We are delighted to announce that FinNLP-2023 will be in conjunction with IJCAI-2023 from 19th-25th August 2023, Macao. This year, we organize a Joint Workshop of The 5th Financial Technology And Natural Language Processing (*FinNLP*) and 2nd *Multimodal AI For Financial Forecasting* (Muffin). Thus, papers related to NLP or multimodal AI in finance are welcome.
This year, we have a shared task related to *multilingual ESG issue identification*. Registration is open now, and the dataset will be released soon. Please refer to our website for more details - FinNLP-2023: https://sites.google.com/nlg.csie.ntu.edu.tw/finnlp-2023/home *Submission Deadline: April 26, 2023* Accepted papers proceedings will be published at ACL Anthology. Best Regards, FinNLP-2023 Organizers FinNLP-MUFFIN-2023: The Joint Workshop of the 5th Financial Technology and Natural Language Processing (FinNLP) and 2nd Multimodal AI For Financial Forecasting (MUFFIN) IJCAI-2023 Macao, August 19-25, 2023 Conference website https://finnlp-muffin-ijcai23.github.io/ Submission link https://easychair.org/conferences/?conf=finnlpmuffin2023 Submission deadline April 26, 2023 *About The FinNLP Workshop* The aim of this workshop is to provide a forum where international participants share knowledge on applying NLP to the FinTech domain. Recently, analyzing documents related to finance and economics has attracted much attention in the AI community. In the financial field, FinTech is a new industry that focuses on improving financial activity with technology. Thus, in order to bridge the gap between the NLP research and the financial applications, we organize FinNLP workshop series. One of the expected accomplishments of FinNLP is to introduce insights from the financial domain to the NLP community. With the sharing of the researchers in FinNLP, the challenging problems of blending FinTech and NLP will be identified, and the future research direction will be shaped. That can broaden the scope of this interdisciplinary research area. *About The Muffin Workshop* The Workshop aims to explore recent advances and challenges of multimodal AI for finance. Financial forecasting is an essential task that helps investors make sound investment decisions and wealth creation. With increasing public interest in trading stocks, cryptocurrencies, bonds, commodities, currencies, crypto coins and non-fungible tokens (NFTs), there have been several attempts to utilize unstructured data for financial forecasting. Unparalleled advances in multimodal deep learning have made it possible to utilize multimedia such as textual reports, news articles, streaming video content, audio conference calls, user social media posts, customer web searches, etc for identifying profit creation opportunities in the market. E.g., how can we leverage new and better information to predict movements in stocks and cryptocurrencies well before others? However, there are several hurdles towards realizing this goal - (1) large volumes of chaotic data, (2) combining text, audio, video, social media posts, and other modalities is non-trivial, (3) long context of media spanning multiple hours, days or even months, (4) user sentiment and media hype-driven stock/crypto price movement and volatility, (5) difficulties with traditional statistical methods (6) misinformation and non-interpretability of financial systems leading to massive losses and bankruptcies. At the IJCAI-2023 Joint Workshop of the 5th Financial Technology and Natural Language Processing (FinNLP) and 2nd Multimodal AI For Financial Forecasting (MUFFIN), we aim to bring bring together researchers from natural language processing, computer vision, speech recognition, machine learning, statistics and quantitative trading communities to expand research on the intersection of AI and finance.Please select a suitable track (“NLP” or “Multimodal”) for best considerations and reviewer matching.We will also organize 2 shared tasks in this workshop – (1) ESG Issue Identification (2) Price and Volatility Prediction From Conference Call Videos. Submission Guidelines Papers submitted to the main track must be formatted according to ACL Guidelines <https://acl-org.github.io/ACLPUB/formatting.html> - *Long Paper*: May consist of up to 8 pages of content, plus unlimited pages for references and appendix. - *Short Paper and Demo Paper*: May consist of up to 4 pages of content, plus unlimited references and appendix. 1. The reviewing process will be double-blind for Long and Short Paper, and single-blind for Demo Paper. Submissions must be in electronic form using the FinNLP-2023 paper submission link above. 2. *No Show Policy*: At least one author of each accepted paper *must* travel to the IJCAI venue in person. Papers with “No Show” will be redacted. Authors will be required to agree to this requirement at the time of submission. Committees *General Chairs - FinNLP* - Chung-Chi Chen <http://nlg.csie.ntu.edu.tw/~cjchen/>, National Institute of Advanced Industrial Science and Technology, Japan - Hiroya Takamura <http://www.lr.pi.titech.ac.jp/~takamura/>, Tokyo Institute of Technology, Japan *General Chairs - Muffin* - Puneet Mathur <http://www.cs.umd.edu/~puneetm/>, University of Maryland College Park, USA - Ramit Sawhney <https://sites.google.com/iiitd.ac.in/ramitsawhney/home?authuser=0>, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi *Organizing Committee* - Dinesh Manocha <https://www.cs.umd.edu/people/dmanocha>, University of Maryland College Park, USA - Preslav Nakov <https://mbzuai.ac.ae/study/faculty/preslav-nakov/>, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi - Hen-Hsen Huang <http://nlg.csie.ntu.edu.tw/~hhhuang/>, Institute of Information Science, Academia Sinica, Taiwan - Hsin-Hsi Chen <http://nlg.csie.ntu.edu.tw/advisor.php>, Department of Computer Science and Information Engineering, National Taiwan University, Taiwan - Hiroki Sakaji <https://tetsuwaka.net/>, School of Engineering, The University of Tokyo, Japan - Kiyoshi Izumi <http://kinba.sakura.ne.jp/mainj/>, School of Engineering, The University of Tokyo, Japan *Advisory Committee* - Franck Dernoncourt <https://research.adobe.com/person/franck-dernoncourt/>, Adobe Research, USA - Fu-Ming Guo <https://www.linkedin.com/in/fumingguo>, Fidelity Investments, USA - Lucie Flek <https://lucieflek.github.io/>, University of Marburg, Germany - Sanghamitra Dutta <https://ece.umd.edu/clark/faculty/1711/Sanghamitra-Dutta>, University of Maryland College Park, USA - Sudheer Chava <https://research.gatech.edu/sudheer-chava>, Georgia Institute of Technology, USA Publication FinNLP-MUFFIN-2023 proceedings will be published in ACL Anthology <https://www.aclweb.org/anthology/>. Venue In conjunction with IJCAI-2023 <https://ijcai-23.org/>, 19th-25th August 2023, Macao Contact All questions about submissions should be emailed to *[email protected]* <[email protected]>
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