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• W3168264548 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." @default.
• W3168264548 created "2021-06-22" @default.
• W3168264548 creator A5052281233 @default.
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• W3168264548 date "2021-01-01" @default.
• W3168264548 modified "2023-10-17" @default.
• W3168264548 title "SentSim: Crosslingual Semantic Evaluation of Machine Translation" @default.
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• W3168264548 doi "https://doi.org/10.18653/v1/2021.naacl-main.252" @default.
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