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              <text>Fine-tuning Language Models for Predicting the Impact of Events Associated to Financial News Articles</text>
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              <text>ESG; ESG impact prediction; financial natural language processing; impact prediction; language models</text>
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              <text>Investors and other stakeholders like consumers and employees, increasingly consider ESG factors when making decisions about investments or engaging with companies. Taking into account the importance of ESG today, FinNLP-KDF introduced the ML-ESG-3 shared task, which seeks to determine the duration of the impact of financial news articles in four languages - English, French, Korean, and Japanese. This paper describes our team, LIPIs approach towards solving the above-mentioned task. Our final systems consist of translation, paraphrasing and fine-tuning language models like BERT, Fin-BERT and RoBERTa for classification. We ranked first in the impact duration prediction subtask for French language.  2024 ELRA Language Resource Association.</text>
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              <text>Banerjee N.; Sarkar A.; Chakraborty S.; Ghosh S.; Naskar S.K.</text>
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              <text>Joint Workshop of the 7th Financial Technology and Natural Language Processing, the 5th Knowledge Discovery from Unstructured Data in Financial Services and the 4th Economics and Natural Language Processing, FinNLP-KDF-ECONLP 2024 at LREC-COLING 2024 - Workshop Proceedings, pp. 244-247.</text>
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              <text>European Language Resources Association (ELRA)</text>
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              <text>2024-01-01</text>
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              <text>Restricted Access</text>
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              <text>ISBN: 978-249381419-7</text>
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              <text>Online</text>
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              <text>Banerjee N., Christ (Deemed to be University), India; Sarkar A., Christ (Deemed to be University), India; Chakraborty S., Christ (Deemed to be University), India; Ghosh S., Jadavpur University, India; Naskar S.K., Jadavpur University, India</text>
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