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.

翻译：同声传译（SimulMT）模型在源句结束前开始翻译，使得翻译与源句保持单调对齐。然而，通用的全句翻译测试集是通过离线翻译整个源句获得的，并非为SimulMT评估而设计，这促使我们思考这是否会低估SimulMT模型的性能。在本文中，我们基于MuST-C英中测试集手动标注了一个单调测试集，记为SiMuST-C。人工评估证实了该标注测试集的可接受性。对三种不同SimulMT模型的评估验证了我们的测试集可以缓解低估问题。进一步实验表明，在自动提取的单调训练集上进行微调，能使SimulMT模型的BLEU值提升高达3分。