Teng-Tsai Tu
2023
Multi-Lingual ESG Issue Identification
Chung-Chi Chen
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Yu-Min Tseng
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Juyeon Kang
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Anaïs Lhuissier
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Min-Yuh Day
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Teng-Tsai Tu
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Hsin-Hsi Chen
Proceedings of the Fifth Workshop on Financial Technology and Natural Language Processing and the Second Multimodal AI For Financial Forecasting
Multi-Lingual ESG Impact Type Identification
Chung-Chi Chen
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Yu-Min Tseng
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Juyeon Kang
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Anaïs Lhuissier
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Yohei Seki
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Min-Yuh Day
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Teng-Tsai Tu
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Hsin-Hsi Chen
Proceedings of the Sixth Workshop on Financial Technology and Natural Language Processing
Assessing a company’s sustainable development goes beyond just financial metrics; the inclusion of environmental, social, and governance (ESG) factors is becoming increasingly vital. The ML-ESG shared task series seeks to pioneer discussions on news-driven ESG ratings, drawing inspiration from the MSCI ESG rating guidelines. In its second edition, ML-ESG-2 emphasizes impact type identification, offering datasets in four languages: Chinese, English, French, and Japanese. Of the 28 teams registered, 8 participated in the official evaluation. This paper presents a comprehensive overview of ML-ESG-2, detailing the dataset specifics and summarizing the performance outcomes of the participating teams.
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Co-authors
- Chung-Chi Chen 2
- Yu-Min Tseng 2
- Juyeon Kang 2
- Anaïs Lhuissier 2
- Min-Yuh Day 2
- show all...