TranSFormer: Slow-Fast Transformer for Machine Translation
Bei Li, Jing Yi, Xu Tan, Zhen Ni Xing, Tong Xiao, Jingbo Zhu · 2023
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 finegrained 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.