TranSFormer: Slow-Fast Transformer for Machine Translation
Bei Li Yi Jing Xu Tan Zhen Xing Tong Xiao Jingbo Zhu
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.