Glancing Future for Simultaneous Machine Translation
This addresses the challenge of forced predictions and reduced global information capture in SiMT models, which is incremental as it builds on existing SiMT methods.
The paper tackles the problem of simultaneous machine translation (SiMT) by proposing a method that glances future in curriculum learning to bridge the gap between seq2seq and prefix2prefix training, resulting in outperforming strong baselines.
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.