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

20 August 2023 (GMT+8)

Location: Room: Almaty 6002, Sheraton Grand Macao, Macau, China

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 task 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 20, 2023: Workshop at IJCAI 2023 (GMT+8)

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

Accepted Papers - Main Track
  1. Breaking the Bank with ChatGPT: Few-Shot Text Classification for Finance Lefteris Loukas, Ilias Stogiannidis, Prodromos Malakasiotis and Stavros Vassos
  2. ChatGPT as Data Augmentation for Compositional Generalization: A Case Study in Open Intent Detection
    Yihao Fang, Xianzhi Li, Stephen Thomas and Xiaodan Zhu
  3. Beyond Classification: Financial Reasoning in State-of-the-Art Language Models
    Guijin Son, Hanearl Jung, Moonjeong Hahm, Keonju Na and Sol Jin
  4. Textual Evidence Extraction for ESG Scores
    Naoki Kannan and Yohei Seki
  5. A Scalable and Adaptive System to Infer the Industry Sectors of Companies: Prompt + Model Tuning of Generative Language Models
    Lele Cao, Vilhelm von Ehrenheim, Astrid Berghult, Cecilia Henje, Richard Anselmo Stahl, Joar Wandborg, Sebastian Stan, Armin Catovic, Erik Ferm and Hannes Ingelhag
  6. LoKI: Money Laundering Report Generation via Logical Table-to-Text using Meta Learning
    Harika Cm, Debasmita Das, Ram Ganesh V, Rajesh Kumar Ranjan and Siddhartha Asthana
  7. DeRisk: An Effective Deep Learning Framework for Credit Risk Prediction over Real-World Financial Data
    Yancheng Liang, Jiajie Zhang, Hui Li, Xiaochen Liu, Yi Hu, Yong Wu, Jiaoyao Zhang, Yongyan Liu and Yi Wu
  8. Reducing tokenizer's tokens per word ratio in Financial domain with T-MuFin BERT Tokenizer
    Braulio Blanco Lambruschini, Patricia Becerra-Sanchez, Mats Brorsson and Maciej Zurad
  9. Using Deep Learning to Find the Next Unicorn: A Practical Synthesis on Optimization Target, Feature Selection, Data Split and Evaluation Strategy
    Lele Cao, Vilhelm von Ehrenheim, Sebastian Krakowski, Xiaoxue Li and Alexandra Lutz
  10. Model-Agnostic Meta-Learning for Natural Language Understanding Tasks in Finance
    Bixing Yan, Shaoling Chen, Yuxuan He and Zhihan Li
Accepted Papers - Shared Task
  1. Leveraging Contrastive Learning with BERT for ESG Issue Identification
    Weiwei Wang, Wenyang Wei, Qingyuan Song and Yansong Wang
  2. Leveraging BERT Language Models for Multi-Lingual ESG Issue Identification
    Elvys Linhares Pontes, Mohamed Benjannet and Lam Kim Ming
  3. EaSyGuide: ESG Issue Identification Framework leveraging Abilities of Generative Large Language Models
    Hanwool Lee, Jonghyun Choi, Sohyeon Kwon and Sungbum Jung
  4. Jetsons at the FinNLP-2023: Using Synthetic Data and Transfer Learning for Multilingual ESG Issue Classification
    Parker Glenn, Alolika Gon, Nikhil Kohli, Sihan Zha, Parag Pravin Dakle and Preethi Raghavan
  5. HKESG at the ML-ESG Task: Exploring Transformer Representations for Multilingual ESG Issue Identification
    Ivan Mashkin and Emmanuele Chersoni
  6. Team HHU at the FinNLP-2023 ML-ESG Task: A Multi-Model Approach to ESG-Key-Issue Classification
    Fabian Billert and Stefan Conrad
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