Introduction

In FinNLP-2022, we proposed a FinSim4-ESG shared task, which is related to the topic of environmental, social, and corporate governance (ESG). To continue exploring ESG topics, FinNLP@IJCAI-2023 shared a new dataset for the FinNLP community to explore the multi-lingual ESG issue identification task. Based on the MSCI ESG rating guidelines, ESG-related news can be classified into 35 ESG key issues. The system needs to be aware of the ESG issues of each article. We used multilingual news articles as the raw material, and conduct annotation on the articles. The target languages include English, Chinese, Japanese, and French. Note that, in the Chinese dataset, we merge issues in SASB Standard into MSCI guidelines. The overview of this shared task series (Chinese) can be found in [1]. 

In ML-ESG-2, we introduce a new task to continue the discussion on ESG rating. The task we proposed is ESG impact type identification. That is, the models need to identify the given news is an opportunity or risk from the ESG aspect. Specifically, Impact Type Identification is a single-choice question. The task aims to ascertain the type of impact a news article might have on the company from the ESG aspect. The possible labels are "Opportunity", "Risk", and "Cannot Distinguish". Note that, labels in the Japanese dataset are "Positive", "Negative", and "N/A". Please refer to [2] for details. 

[1] Yu-Min Tseng, Chung-Chi Chen, Hen-Hsen Huang, and Hsin-Hsi Chen. 2023. DynamicESG: A Dataset for Dynamically Unearthing ESG Ratings from News Articles. In Proceedings of The 32nd ACM International Conference on Information and Knowledge Management (CIKM'23).

[2] Naoki Kannan and Yohei Seki. 2023. Textual Evidence Extraction for ESG Scores. In Proceedings of The 5th Workshop on Financial Technology and Natural Language Processing (FinNLP).

Registration

Registration Form: https://forms.gle/j6gL5jy1upq5LrKY9

Important Dates: Time zone: Anywhere On Earth (AOE)

• Registration Open: July 18, 2023

• Training set release (Chinese): July 31, 2023

• Training set release (English & French): Aug. 21, 2023

• Test set release: Sep 20, 2023

• System's outputs submission deadline: Sep 25, 2023

• Release of results: Sep 28, 2023

• Shared task paper submissions due: Oct 3, 2023 - Shared Task Paper Submission System: Available soon

• Notification: Oct 5, 2023

• Camera-Ready Version of Shared Task Paper Due: Oct 8, 2023

Policies

• The reviewing process will be single-blind. Accepted papers proceedings will be published at ACL Anthology.

• Shared task participants will be asked to review other teams' papers during the review period.  

• Submissions must be in electronic form using the FinNLP-2023 paper submission software linked above. 

• At least one author of each accepted paper should register and present their work (either online or in-person) in FinNLP-2023. Papers with “No Show” may be redacted. Authors will be required to agree to this requirement at the time of submission. 

Shared Task Organizers

• Chung-Chi Chen - AIRC, AIST, Japan

  • Yu-Min Tseng - Department of Computer Science and Information Engineering, National Taiwan University, Taiwan

• Juyeon KANG - 3DS Outscale (ex Fortia)

• Anaïs Lhuissier - 3DS Outscale (ex Fortia)

• Min-Yuh Day - Graduate Institute of Information Management, National Taipei University

• Teng-Tsai Tu - Graduate Institute of Information Management, National Taipei University

  • Yohei Seki - University of Tsukuba

• Hsin-Hsi Chen - Department of Computer Science and Information Engineering, National Taiwan University, Taiwan