In simultaneous machine translation, the objective is to determine when to
produce a partial translation given a continuous stream of source words, with a
trade-off between latency and quality. We propose a neural machine translation
(NMT) model that makes dynamic decisions when to continue feeding on input or
generate output words. The model is composed of two main components: one to
dynamically decide on ending a source chunk, and another that translates the
consumed chunk. We train the components jointly and in a manner consistent with
the inference conditions. To generate chunked training data, we propose a
method that utilizes word alignment while also preserving enough context. We
compare models with bidirectional and unidirectional encoders of different
depths, both on real speech and text input. Our results on the IWSLT 2020
English-to-German task outperform a wait-k baseline by 2.6 to 3.7% BLEU
absolute.

提出了一种用于机器翻译的神经网络模型，其能够动态决定何时继续输入源文本或生成翻译文本，通过使用单向或双向编码器来处理实际语音和文本输入，使用词对齐方法生成分块训练数据，并在 IWSLT 2020 英 - 德任务上优于 wait-k 基线 2.6 到 3.7% BLEU 中的结果。