Glancing Future for Simultaneous Machine Translation

TL;DR

This paper introduces a curriculum learning approach that gradually shifts from full-sequence to prefix-based training in simultaneous machine translation, improving model performance by bridging the training gap.

Contribution

We propose a novel 'glancing future' curriculum learning method that transitions SiMT models from seq2seq to prefix2prefix training, enhancing translation quality.

Findings

Outperforms strong baseline methods

Applicable to various SiMT approaches

Improves translation accuracy and robustness

Abstract

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.