@inproceedings{li2023transformer,
author = {Li, Bei and Jing, Yi and Tan, Xu and Xing, Zhen and Xiao, Tong and Zhu, Jingbo},
title = {TranSFormer: Slow-Fast Transformer for Machine Translation},
booktitle = {ACL 2023},
year = {2023},
month = {July},
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 \textbf[S]low-\textbf[F]ast two-stream learning model, referred to as Tran\textbf[SF]ormer, 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.},
url = {http://approjects.co.za/?big=en-us/research/publication/transformer-slow-fast-transformer-for-machine-translation/},
}