论文标题
文档级神经机器翻译的话语凝聚力评估
Discourse Cohesion Evaluation for Document-Level Neural Machine Translation
论文作者
论文摘要
众所周知，由出色的文档级神经机器翻译（NMT）模型产生的翻译是一致且连贯的。但是，像BLEU这样的现有句子级评估指标几乎无法在文档级别反映模型的性能。为了解决这个问题，我们在本文中提出了一种话语凝聚力评估方法（DCOEM），并贡献了一个新的测试套件，该套件考虑了四个凝聚力的方式（参考，连接，替代和词汇凝聚力），以衡量文档翻译的凝聚力。最近的文档级NMT系统的评估结果表明，我们的方法在估计文档级别的翻译方面是实用且至关重要的。
It is well known that translations generated by an excellent document-level neural machine translation (NMT) model are consistent and coherent. However, existing sentence-level evaluation metrics like BLEU can hardly reflect the model's performance at the document level. To tackle this issue, we propose a Discourse Cohesion Evaluation Method (DCoEM) in this paper and contribute a new test suite that considers four cohesive manners (reference, conjunction, substitution, and lexical cohesion) to measure the cohesiveness of document translations. The evaluation results on recent document-level NMT systems show that our method is practical and essential in estimating translations at the document level.
