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    <abstract>Learning multiscale Transformer models has been evidenced as a viable approach to augmenting machine translation systems. Prior research has primarily focused on treating subwords as basic units in developing such systems. However, the incorporation of fine-grained character-level features into multiscale Transformer has not yet been explored. In this work, we present a Slow-Fast two-stream learning model, referred to as TranSFormer, which utilizes a “slow” branch to deal with subword sequences and a “fast” branch to deal with longer character sequences. This model is efficient since the fast branch is very lightweight by reducing the model width, and yet provides useful fine-grained features for the slow branch. Our TranSFormer shows consistent BLEU improvements (larger than 1 BLEU point) on several machine translation benchmarks.</abstract>
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%0 Conference Proceedings
%T TranSFormer: Slow-Fast Transformer for Machine Translation
%A Li, Bei
%A Jing, Yi
%A Tan, Xu
%A Xing, Zhen
%A Xiao, Tong
%A Zhu, Jingbo
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F li-etal-2023-transformer
%X Learning multiscale Transformer models has been evidenced as a viable approach to augmenting machine translation systems. Prior research has primarily focused on treating subwords as basic units in developing such systems. However, the incorporation of fine-grained character-level features into multiscale Transformer has not yet been explored. In this work, we present a Slow-Fast two-stream learning model, referred to as TranSFormer, which utilizes a “slow” branch to deal with subword sequences and a “fast” branch to deal with longer character sequences. This model is efficient since the fast branch is very lightweight by reducing the model width, and yet provides useful fine-grained features for the slow branch. Our TranSFormer shows consistent BLEU improvements (larger than 1 BLEU point) on several machine translation benchmarks.
%R 10.18653/v1/2023.findings-acl.430
%U https://aclanthology.org/2023.findings-acl.430
%U https://doi.org/10.18653/v1/2023.findings-acl.430
%P 6883-6896

Markdown (Informal)

[TranSFormer: Slow-Fast Transformer for Machine Translation](https://aclanthology.org/2023.findings-acl.430) (Li et al., Findings 2023)

• TranSFormer: Slow-Fast Transformer for Machine Translation (Li et al., Findings 2023)

ACL

• Bei Li, Yi Jing, Xu Tan, Zhen Xing, Tong Xiao, and Jingbo Zhu. 2023. TranSFormer: Slow-Fast Transformer for Machine Translation. In Findings of the Association for Computational Linguistics: ACL 2023, pages 6883–6896, Toronto, Canada. Association for Computational Linguistics.