Discourse Cohesion Evaluation for Document-Level Neural Machine Translation
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 8 of its 29 references that are in the map. First posted on arXiv 19 Aug 2022.
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8 of 29 references.
References · 8
- 2017Attention Is All You Needcited by 60,361
- 2018Has Machine Translation Achieved Human Parity? A Case for Document-level Evaluationcited by 88
- 2017Evaluating Discourse Phenomena in Neural Machine Translationcited by 87
- 2018Improving the Transformer Translation Model with Document-Level Contextcited by 74
- 2018Attaining the Unattainable? Reassessing Claims of Human Parity in Neural Machine Translationcited by 58
- 2019Evaluating Pronominal Anaphora in Machine Translation: An Evaluation Measure and a Test Suitecited by 9
- 2019A Test Suite and Manual Evaluation of Document-Level NMT at WMT19cited by 2
- 2019SAO WMT19 Test Suite: Machine Translation of Audit Reportscited by 1
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Citing papers per year: 2026: 1.
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