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ML-ESG 2024 : FinNLP-KDF@LREC-COLING 2024 Shared Task: ML-ESG 3

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Link: https://sites.google.com/nlg.csie.ntu.edu.tw/finnlp-kdf-2024/shared-task-ml-esg-3
 
When May 20, 2024 - May 25, 2024
Where Torina, Italy
Submission Deadline TBD
Categories    natural language processing   financial documents   ESG   multilingual dataset
 

Call For Papers

Call for participation:
FinNLP-KDF@LREC-COLING 2024 Shared Task: ML-ESG3 - Multi-Lingual ESG Impact Type and Duration Inference


Practical Information:
To be held as part of the Joint Workshop of the 7th Financial Technology and Natural Language Processing (FinNLP) and the 5th Knowledge Discovery from Unstructured Data in Financial Services (KDF)
during the LREC-COLING-2024, Torina, Italy, from 20th May to 25th May, 2024. It is a one-day event of which the exact date is to be announced.
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Shared Task URL: https://sites.google.com/nlg.csie.ntu.edu.tw/finnlp-kdf-2024/shared-task-ml-esg-3
Workshop URL: https://sites.google.com/nlg.csie.ntu.edu.tw/finnlp-kdf-2024/home
Participation Form: https://docs.google.com/forms/d/e/1FAIpQLSdnndCuMPxZH4q-Rx8mfTyDgwRUb44jYWSrH2RcjdmcmM-vDQ/viewform (now open as of 25/11/2023)
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Shared Task Description:
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 conducted annotations 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. In ML-ESG-2, we introduced 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 as an opportunity or risk from the ESG aspect. In ML-ESG-3, we seek to determine the level (low, medium, high) and duration (length: "Less than 2 years", "2 to 5 years", and "More than 5 years") of the impact an event in the news article might have on the company. More details are given below. Additionally, in this new edition, we have added a new dataset for Korean ESG news articles.
Dataset
The design of the task may be slightly different among all subsets. Below are the introductions of the task design.
Chinese: Based on the distinction between short-term and long-term defined, we present three labels: "Less than 2 years", "2 to 5 years", and "More than 5 years". [1]
English & French: There are two annotations in this dataset, "Impact Level" and "Impact Length." Impact Length was selected from "Less than 2 years", "2 to 5 years", and "More than 5 years", which is the same as the Chinese dataset. Impact Level qualifies the opportunity or risk as being of "low", "medium" or "high." Please refer to our guidelines for more details. [2]
Japanese: Based on the distinction between short-term and long-term defined, we present three labels: "Less than 2 years", "2 to 5 years", and "More than 5 years". [1] The labels proposed in [3] are also shared for further exploration.
Korean: In this dataset, there are two annotations: 'Impact Type' and 'Impact Length.' 'Impact Type' is categorized as 'opportunity,' 'risk,' or 'cannot distinguish,' while 'Impact Length' is classified into three durations: 'less than 2 years', '2 to 5 years', and 'more than 5 years'. For more detailed information, please refer to the following source [1]
[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] ML-ESG 2024 for Social Good (ESG) - 3rd Edition Guidelines by 3DS Outscale
[3] 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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Registration Open: Nov. 25th, 2023
1st Call for papers & shared task participants: Nov. 25th, 2023
2nd Call for papers & shared task participants: Dec. 15th, 2023
Training set release: Dec. 13th, 2023
Test set release: Feb. 8th, 2024
System's outputs submission deadline (Registration Close): Feb. 20th
Release of results: Feb. 25th
Shared task paper submissions due: March 7th
Notification: March 20th
Camera-Ready Version of Shared Task Paper Due: March 25th

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Contact:
For any questions on the shared task please contact us on:
ml.esg.task@gmail.com
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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, France
Anaïs Lhuissier - 3DS Outscale, France
Hanwool Lee - NCSOFT, South Korea
Min-Yuh Day - Graduate Institute of Information Management, National Taipei University, Taiwan
Teng-Tsai Tu - Graduate Institute of Information Management, National Taipei University, Taiwan
Yohei Seki - University of Tsukuba, Japan
Hsin-Hsi Chen - Department of Computer Science and Information Engineering, National Taiwan University, Taiwan



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