Abstract
Though machine translation errors caused by the lack of context beyond one sentence have long been acknowledged, the development of context-aware NMT systems is hampered by several problems. Firstly, standard metrics are not sensitive to improvements in consistency in document-level translations. Secondly, previous work on context-aware NMT assumed that the sentence-aligned parallel data consisted of complete documents while in most practical scenarios such document-level data constitutes only a fraction of the available parallel data. To address the first issue, we perform a human study on an English-Russian subtitles dataset and identify deixis, ellipsis and lexical cohesion as three main sources of inconsistency. We then create test sets targeting these phenomena. To address the second shortcoming, we consider a set-up in which a much larger amount of sentence-level data is available compared to that aligned at the document level. We introduce a model that is suitable for this scenario and demonstrate major gains over a context-agnostic baseline on our new benchmarks without sacrificing performance as measured with BLEU.
Abstract (translated)
虽然人们早就认识到一句话以外的上下文缺失会导致机器翻译错误，但是上下文感知的NMT系统的发展却受到若干问题的阻碍。首先，标准度量对文档级翻译的一致性改进不敏感。第二，之前关于上下文感知的NMT的研究假设句子对齐的并行数据由完整的文档组成，而在大多数实际场景中，这些文档级数据仅构成可用并行数据的一小部分。为了解决第一个问题，我们对英俄字幕数据集进行了人类研究，并将指示、省略和词汇衔接作为三个主要的不一致来源。然后我们创建针对这些现象的测试集。为了解决第二个缺点，我们考虑了一种设置，与在文档级别对齐的设置相比，在这种设置中可以获得更多的句子级别数据。我们引入了一个适合这个场景的模型，并展示了在新基准上相对于上下文无关基线的主要收益，同时不牺牲用BLEU度量的性能。
URL
https://arxiv.org/abs/1905.05979
