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.

本文提出了一种自适应前缀训练策略（LEAPT），使机器翻译模型学习如何翻译源前缀并利用未来上下文，实验证明相比竞争基线表现更好并取得了良好的结果。

LEAPT: 学习自适应前缀到前缀转换，用于同时机器翻译

LEAPT: Learning Adaptive Prefix-to-prefix Translation For Simultaneous Machine Translation

The advent of context-aware NMT has resulted in promising improvements in the
overall translation quality and specifically in the translation of discourse
phenomena such as pronouns. Previous works have mainly focused on the use of
past sentences as context with a focus on anaphora translation. In this work,
we investigate the effect of future sentences as context by comparing the
performance of a contextual NMT model trained with the future context to the
one trained with the past context. Our experiments and evaluation, using
generic and pronoun-focused automatic metrics, show that the use of future
context not only achieves significant improvements over the context-agnostic
Transformer, but also demonstrates comparable and in some cases improved
performance over its counterpart trained on past context. We also perform an
evaluation on a targeted cataphora test suite and report significant gains over
the context-agnostic Transformer in terms of BLEU.

本研究探讨了将未来句子作为上下文与使用过去句子作为上下文的翻译模型效果，结果表明使用未来句子作为上下文不仅可以显著改善翻译效果，而且在某些情況下比过去句子更有效。同时，我们还在特定情况下的测试中获得了显著的改善。