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.
@article{arxiv.2208.09118,
title = {Discourse Cohesion Evaluation for Document-Level Neural Machine Translation},
author = {Xin Tan and Longyin Zhang and Guodong Zhou},
journal= {arXiv preprint arXiv:2208.09118},
year = {2022}
}