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%0 Conference Proceedings
%T Reducing tokenizer’s tokens per word ratio in Financial domain with T-MuFin BERT Tokenizer
%A Lambruschini, Braulio Blanco
%A Becerra-Sanchez, Patricia
%A Brorsson, Mats
%A Zurad, Maciej
%Y Chen, Chung-Chi
%Y Takamura, Hiroya
%Y Mathur, Puneet
%Y Sawhney, Remit
%Y Huang, Hen-Hsen
%Y Chen, Hsin-Hsi
%S Proceedings of the Fifth Workshop on Financial Technology and Natural Language Processing and the Second Multimodal AI For Financial Forecasting
%D 2023
%8 20 aug
%I -
%C Macao
%F gopalakrishnan-etal-2023-reducing
%U https://aclanthology.org/2023.finnlp-1.9
%P 94-103

Markdown (Informal)

[Reducing tokenizer’s tokens per word ratio in Financial domain with T-MuFin BERT Tokenizer](https://aclanthology.org/2023.finnlp-1.9) (Lambruschini et al., FinNLP-WS 2023)

• Reducing tokenizer’s tokens per word ratio in Financial domain with T-MuFin BERT Tokenizer (Lambruschini et al., FinNLP-WS 2023)

ACL

• Braulio Blanco Lambruschini, Patricia Becerra-Sanchez, Mats Brorsson, and Maciej Zurad. 2023. Reducing tokenizer’s tokens per word ratio in Financial domain with T-MuFin BERT Tokenizer. In Proceedings of the Fifth Workshop on Financial Technology and Natural Language Processing and the Second Multimodal AI For Financial Forecasting, pages 94–103, Macao. -.