Simultaneous machine translation (SimulMT) speeds up the translation process
by starting to translate before the source sentence is completely available. It
is difficult due to limited context and word order difference between
languages. Existing methods increase latency or introduce adaptive read-write
policies for SimulMT models to handle local reordering and improve translation
quality. However, the long-distance reordering would make the SimulMT models
learn translation mistakenly. Specifically, the model may be forced to predict
target tokens when the corresponding source tokens have not been read. This
leads to aggressive anticipation during inference, resulting in the
hallucination phenomenon. To mitigate this problem, we propose a new framework
that decompose the translation process into the monotonic translation step and
the reordering step, and we model the latter by the auxiliary sorting network
(ASN). The ASN rearranges the hidden states to match the order in the target
language, so that the SimulMT model could learn to translate more reasonably.
The entire model is optimized end-to-end and does not rely on external aligners
or data. During inference, ASN is removed to achieve streaming. Experiments
show the proposed framework could outperform previous methods with less
latency.

本文提出了一种将翻译过程分解为单调翻译和重新排序步骤的新框架，并通过辅助排序网络模型来模拟其中的重新排序步骤，从而提高了同时翻译模型的翻译效果。

无需预测的同步机器翻译训练

Anticipation-Free Training for Simultaneous Machine Translation

In this paper, we present Neural Phrase-based Machine Translation (NPMT). Our
method explicitly models the phrase structures in output sequences using
Sleep-WAke Networks (SWAN), a recently proposed segmentation-based sequence
modeling method. To mitigate the monotonic alignment requirement of SWAN, we
introduce a new layer to perform (soft) local reordering of input sequences.
Different from existing neural machine translation (NMT) approaches, NPMT does
not use attention-based decoding mechanisms. Instead, it directly outputs
phrases in a sequential order and can decode in linear time. Our experiments
show that NPMT achieves superior performances on IWSLT 2014
German-English/English-German and IWSLT 2015 English-Vietnamese machine
translation tasks compared with strong NMT baselines. We also observe that our
method produces meaningful phrases in output languages.

本文提出神经短语机器翻译模型 (NPMT)，采用基于分段的序列建模方法 (SWAN) 显式建模翻译序列中的短语结构并引入新层实现局部重新排序 (Soft)。相比于基于注意力机制的 NMT 方法，NPMT 直接以顺序方式输出短语，因此可以在线性时间内解码，实验表明与强 NMT 基准模型相比，在 IWSLT 2014 德英 / 英德和 IWSLT 2015 英越机器翻译任务上表现优越，并且 NPMT 模型的输出翻译语言有意义。