Simultaneous machine translation (SimulMT) models start translation before
the end of the source sentence, making the translation monotonically aligned
with the source sentence. However, the general full-sentence translation test
set is acquired by offline translation of the entire source sentence, which is
not designed for SimulMT evaluation, making us rethink whether this will
underestimate the performance of SimulMT models. In this paper, we manually
annotate a monotonic test set based on the MuST-C English-Chinese test set,
denoted as SiMuST-C. Our human evaluation confirms the acceptability of our
annotated test set. Evaluations on three different SimulMT models verify that
the underestimation problem can be alleviated on our test set. Further
experiments show that finetuning on an automatically extracted monotonic
training set improves SimulMT models by up to 3 BLEU points.

本文介绍了同时机器翻译（SimulMT）模型，包括其与源语句的单调对齐、测试方法的缺陷、以及人工标注的单调测试集 SiMuST-C 的验证、自动提取的单调训练集的优化等。实验表明，自动提取的单调训练集可以为 SimulMT 模型带来高达 3 个 BLEU 分数的提升。