Abstract
Environmental, Social and Governance reports have to be periodically released by financial companies, as they represent an essential guide for the potential, socially-responsible new investors. Therefore, automatizing the analysis of reports and extracting the main ESG issues mentioned in the text is a goal of primary importance for financial Natural Language Processing (NLP) systems.
In this paper, we report our experiments for the FinSim4-ESG Shared Task, dedicated to the problem of multilingual ESG issue identification in English and French. Our results show that even simple classifiers trained on multilingual data and using crosslingual Transformer representations can achieve a strong performance in the task.
In this paper, we report our experiments for the FinSim4-ESG Shared Task, dedicated to the problem of multilingual ESG issue identification in English and French. Our results show that even simple classifiers trained on multilingual data and using crosslingual Transformer representations can achieve a strong performance in the task.
| Original language | English |
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| Title of host publication | Proceedings of the IJCAI Joint Workshop on Financial Technology and Natural Language Processing (FinNLP) and Multimodal AI for Financial Forecasting (MUFFIN) |
| Publisher | Association for Computational Linguistics (ACL) |
| Publication status | Published - Jul 2023 |
| Event | The Joint Workshop of the 5th Financial Technology and Natural Language Processing (FinNLP) and 2nd Multimodal AI For Financial Forecasting (MUFFIN) - Sheraton Grand Macau, Macau, China Duration: 20 Aug 2023 → … https://finnlp-muffin-ijcai23.github.io/ |
Conference
| Conference | The Joint Workshop of the 5th Financial Technology and Natural Language Processing (FinNLP) and 2nd Multimodal AI For Financial Forecasting (MUFFIN) |
|---|---|
| Abbreviated title | FinNLP 2023 |
| Country/Territory | China |
| City | Macau |
| Period | 20/08/23 → … |
| Internet address |