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Discourse Cohesion Evaluation for Document-Level Neural Machine Translation

2022-08-19 01:56:00
Xin Tan, Longyin Zhang, Guodong Zhou
     

Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2208.09118

PDF

https://arxiv.org/pdf/2208.09118.pdf


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