Discourse Cohesion Evaluation for Document-Level Neural Machine Translation

• URL: http://arxiv.org/abs/2208.09118v1
• Date: Fri, 19 Aug 2022 01:56:00 GMT
• Title: Discourse Cohesion Evaluation for Document-Level Neural Machine Translation
• Authors: Xin Tan and Longyin Zhang and Guodong Zhou
• Abstract summary: It is well known that translations generated by an excellent document-level neural machine translation (NMT) model are consistent and coherent. Existing sentence-level evaluation metrics like BLEU can hardly reflect the model's performance at the document level. We propose a new test suite that considers four cohesive manners to measure the cohesiveness of document translations.
• Score: 36.96887050831173
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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