Can neural machine translation do simultaneous translation?
Kyunghyun Cho Masha Esipova New York University
Abstract
We investigate the potential of attention-based neural machine translation in simultaneous translation. We introduce a novel decoding algorithm, called simultaneous greedy decoding, that allows an existing neural machine translation model to begin translating before a full source sentence is received. This approach is unique from previous works on simultaneous translation in that segmentation and translation are done jointly to maximize the translation quality and that translating each segment is strongly conditioned on all the previous segments. This paper presents a first step toward building a full simultaneous translation system based on neural machine translation.
中文速览
同声传译系统需要在尚未听完整句话时就开始输出译文，而现有方法把"分段"和"翻译"拆成两个独立模块，导致上下文信息断裂、译文质量受损。这项工作提出了一种名为"同步贪心解码（simultaneous greedy decoding）"的新算法，让已有的基于注意力机制的神经机器翻译模型无需重新训练就能直接用于同声传译：算法边接收源语言词、边判断当前信息是否足以生成下一个译词，分段与翻译联合完成，且每个输出词都完整依赖此前所有已读入的上下文。在英捷、英德、英俄三对语言上的实验表明，通过调节"步长"和"初始读入量"两个超参数，可以灵活权衡译文质量与翻译延迟。这项工作证明了神经机器翻译天然具备同声传译的潜力，为后续训练专用同传策略奠定了基础。
原文 arXiv:1606.02012;中英对照 + 大白话阅读 https://aha.fim.ai/paper/1606.02012v1