SentSim: Crosslingual Semantic Evaluation of Machine Translation

Yurun Song, Junchen Zhao, Lucia Specia


Abstract
Machine translation (MT) is currently evaluated in one of two ways: in a monolingual fashion, by comparison with the system output to one or more human reference translations, or in a trained crosslingual fashion, by building a supervised model to predict quality scores from human-labeled data. In this paper, we propose a more cost-effective, yet well performing unsupervised alternative SentSim: relying on strong pretrained multilingual word and sentence representations, we directly compare the source with the machine translated sentence, thus avoiding the need for both reference translations and labelled training data. The metric builds on state-of-the-art embedding-based approaches – namely BERTScore and Word Mover’s Distance – by incorporating a notion of sentence semantic similarity. By doing so, it achieves better correlation with human scores on different datasets. We show that it outperforms these and other metrics in the standard monolingual setting (MT-reference translation), a well as in the source-MT bilingual setting, where it performs on par with glass-box approaches to quality estimation that rely on MT model information.
Anthology ID:
2021.naacl-main.252
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3143–3156
Language:
URL:
https://aclanthology.org/2021.naacl-main.252
DOI:
10.18653/v1/2021.naacl-main.252
Bibkey:
Cite (ACL):
Yurun Song, Junchen Zhao, and Lucia Specia. 2021. SentSim: Crosslingual Semantic Evaluation of Machine Translation. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3143–3156, Online. Association for Computational Linguistics.
Cite (Informal):
SentSim: Crosslingual Semantic Evaluation of Machine Translation (Song et al., NAACL 2021)
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PDF:
https://preview.aclanthology.org/update-css-js/2021.naacl-main.252.pdf
Optional supplementary data:
 2021.naacl-main.252.OptionalSupplementaryData.pdf