STACL: Simultaneous Translation with Implicit Anticipation and Controllable Latency using Prefix-to-Prefix Framework

Mingbo Ma, Liang Huang, Hao Xiong, Renjie Zheng, Kaibo Liu, Baigong Zheng, Chuanqiang Zhang, Zhongjun He, Hairong Liu, Xing Li, Hua Wu, Haifeng Wang

Abstract:

Simultaneous translation, which translates
sentences before they are finished, is useful in many scenarios but is notoriously difficult due to word-order differences. While
the conventional seq-to-seq framework is only
suitable for full-sentence translation, we propose a novel prefix-to-prefix framework for simultaneous translation that implicitly learns
to anticipate in a single translation model.
Within this framework, we present a very simple yet surprisingly effective “wait-k” policy
trained to generate the target sentence concurrently with the source sentence, but always k
words behind. Experiments show our strategy achieves low latency and reasonable quality (compared to full-sentence translation) on
4 directions: zh↔en and de↔en.