Shared Task for Cross-lingual Classification of Corporate Social Responsibility (CSR) Themes and Topics

Yola Nayekoo, Sophia Katrenko, Veronique Hoste, Aaron Maladry, Els Lefever

Abstract
This paper provides an overview of the Shared Task for Cross-lingual Classification of CSR Themes and Topics. We framed the task as two separate sub-tasks: one cross-lingual multi-class CSR theme recognition task for English, French and simplified Chinese and one multi-label fine-grained classification task of CSR topics for Environment (ENV) and Labor and Human Rights (LAB) themes in English. The participants were provided with URLs and annotations for both tasks. Several teams downloaded the data, of which two teams submitted a system for both sub-tasks. In this overview paper, we discuss the set-up of the task and our main findings.
Anthology ID:
2024.finnlp-1.32
Volume:
Proceedings of the Joint Workshop of the 7th Financial Technology and Natural Language Processing, the 5th Knowledge Discovery from Unstructured Data in Financial Services, and the 4th Workshop on Economics and Natural Language Processing
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Chung-Chi Chen, Xiaomo Liu, Udo Hahn, Armineh Nourbakhsh, Zhiqiang Ma, Charese Smiley, Veronique Hoste, Sanjiv Ranjan Das, Manling Li, Mohammad Ghassemi, Hen-Hsen Huang, Hiroya Takamura, Hsin-Hsi Chen
Venue:
FinNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
283–291
Language:
URL:
https://aclanthology.org/2024.finnlp-1.32
DOI:
Bibkey:
Cite (ACL):
Yola Nayekoo, Sophia Katrenko, Veronique Hoste, Aaron Maladry, and Els Lefever. 2024. Shared Task for Cross-lingual Classification of Corporate Social Responsibility (CSR) Themes and Topics. In Proceedings of the Joint Workshop of the 7th Financial Technology and Natural Language Processing, the 5th Knowledge Discovery from Unstructured Data in Financial Services, and the 4th Workshop on Economics and Natural Language Processing, pages 283–291, Torino, Italia. Association for Computational Linguistics.
Cite (Informal):
Shared Task for Cross-lingual Classification of Corporate Social Responsibility (CSR) Themes and Topics (Nayekoo et al., FinNLP 2024)
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