Glancing Future for Simultaneous Machine Translation
Shoutao Guo Shaolei Zhang Yang Feng
Simultaneous machine translation (SiMT) outputs translation while reading the
source sentence. Unlike conventional sequence-to-sequence (seq2seq) training,
existing SiMT methods adopt the prefix-to-prefix (prefix2prefix) training,
where the model predicts target tokens based on partial source tokens. However,
the prefix2prefix training diminishes the ability of the model to capture
global information and introduces forced predictions due to the absence of
essential source information. Consequently, it is crucial to bridge the gap
between the prefix2prefix training and seq2seq training to enhance the
translation capability of the SiMT model. In this paper, we propose a novel
method that glances future in curriculum learning to achieve the transition
from the seq2seq training to prefix2prefix training. Specifically, we gradually
reduce the available source information from the whole sentence to the prefix
corresponding to that latency. Our method is applicable to a wide range of SiMT
methods and experiments demonstrate that our method outperforms strong
baselines.