IJCAI-2023 Joint Workshop of the 5th Financial Technology and Natural Language Processing (FinNLP) and 2nd Multimodal AI For Financial Forecasting (Muffin)

19-25 August, 2023

Location: Macau

News
  • March 30, 2023: IJCAI 2023 Workshop Website Open
  • April 26 May 15, 2023: Submission Deadline Extended, visit CFP for more detail.
  • June 4, 2023: Paper notification
  • June 10, 2023: Camera-Ready Deadline
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 together researchers from multimodal AI community (natural language processing, computer vision, speech recognition, machine learning, statistics and quantitative trading) to expand research on the intersection of AI and finance.

We will also organize a shared tasks in this workshop on ESG Issue Identification.

Please refer to the call for papers and shared task pages for more information.

Important dates
  • March 30, 2023: IJCAI 2023 Workshop Website Open
  • April 26 May 15, 2023: Submission Deadline Extended, visit CFP for more detail.
  • June 4, 2023: Paper notification
  • June 10, 2023: Camera-Ready Deadline
  • June 15, 2023: Early Registration Deadline
  • August 19-25, 2023: Workshop at IJCAI 2023

All deadlines are end of day, anywhere on earth (UTC-12).

General Chairs - FinNLP
  • Chung-Chi Chen, National Institute of Advanced Industrial Science and Technology, Japan
  • Hiroya Takamura, National Institute of Advanced Industrial Science and Technology, Japan
General Chairs - Muffin
  • Puneet Mathur, University of Maryland College Park, USA
  • Ramit Sawhney, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi
Organizing Committee
  • Dinesh Manocha, University of Maryland College Park, USA
  • Preslav Nakov, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi
  • Hen-Hsen Huang, Institute of Information Science, Academia Sinica, Taiwan
  • Hsin-Hsi Chen, Department of Computer Science and Information Engineering, National Taiwan University, Taiwan
  • Hiroki Sakaji, School of Engineering, The University of Tokyo, Japan
  • Kiyoshi Izumi, School of Engineering, The University of Tokyo, Japan
Advisory Committee

Contact us: finnlp.muffin.ijcai2023@gmail.com