Simultaneous machine translation, which aims at a real-time translation, is useful in many live scenarios but very challenging due to the trade-off between accuracy and latency. To achieve the balance for both, the model needs to wait for appropriate streaming text (READ policy) and then generates its translation (WRITE policy). However, WRITE policies of previous work either are specific to the method itself due to the end-to-end training or suffer from the input mismatch between training and decoding for the non-end-to-end training. Therefore, it is essential to learn a generic and better WRITE policy for simultaneous machine translation. Inspired by strategies utilized by human interpreters and "wait" policies, we propose a novel adaptive prefix-to-prefix training policy called LEAPT, which allows our machine translation model to learn how to translate source sentence prefixes and make use of the future context. Experiments show that our proposed methods greatly outperform competitive baselines and achieve promising results.

翻译：同声传译旨在实现实时翻译，在众多实时场景中具有重要应用价值，但由于准确性与延迟性之间的权衡，极具挑战性。为兼顾二者平衡，模型需要等待合适的流式文本（读取策略）后生成译文（写入策略）。然而，现有研究的写入策略或因端到端训练而局限于方法本身，或因非端到端训练面临训练与解码阶段的输入不匹配问题。因此，学习通用且更优的同声传译写入策略至关重要。受人工译员策略与"等待"策略启发，我们提出名为LEAPT的新型自适应前缀到前缀训练策略，该策略使机器翻译模型能够学习翻译源语句前缀并利用未来上下文。实验表明，所提方法显著优于竞争基线，取得了令人满意的结果。