Multi-Lingual ESG Impact Type Identification (ML-ESG-2)

Shared Task


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).


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