FinDeBERTaV2: Word-Segmentation-Free Pre-trained Language Model for Finance

Author:

Suzuki Masahiro1,Sakaji Hiroki1,Hirano Masanori1,Izumi Kiyoshi1

Affiliation:

1. School of Engineering, The University of Tokyo

Publisher

Japanese Society for Artificial Intelligence

Reference63 articles.

1. [Abadji 21] Abadji, J., Su´arez, P. J. O., Romary, L., and Sagot, B.: Ungoliant: An optimized pipeline for the generation of a very largescale multilingual web corpus, in Proceedings of the Workshop on Challenges in the Management of Large Corpora (CMLC-9), pp. 1–9 (2021)

2. [Abadji 22] Abadji, J., Ortiz Suarez, P., Romary, L., and Sagot, B.: Towards a Cleaner Document-Oriented Multilingual Crawled Corpus, in Proceedings of the 13th Language Resources and Evaluation Conference, pp. 4344–4355 (2022)

3. [Araci 19] Araci, D.: FinBERT: Financial Sentiment Analysis with Pre-trained Language Models, arXiv preprint: 1908.10063 (2019)

4. [Beltagy 19] Beltagy, I., Lo, K., and Cohan, A.: SciBERT: A Pretrained Language Model for Scientific Text, in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 3615–3620 (2019)

5. [Bingler 22] Bingler, J. A., Kraus, M., Leippold, M., and Webersinke, N.: Cheap talk and cherry-picking: What ClimateBert has to say on corporate climate risk disclosures, Finance Research Letters, Vol. 47, p. 102776 (2022)

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