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        <title>Metric for Automatic Machine Translation Evaluation based on Universal Sentence Representations</title>
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        <namePart type="given">Hiroki</namePart>
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            <namePart type="given">Silvio</namePart>
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    <abstract>Sentence representations can capture a wide range of information that cannot be captured by local features based on character or word N-grams. This paper examines the usefulness of universal sentence representations for evaluating the quality of machine translation. Al-though it is difficult to train sentence representations using small-scale translation datasets with manual evaluation, sentence representations trained from large-scale data in other tasks can improve the automatic evaluation of machine translation. Experimental results of the WMT-2016 dataset show that the proposed method achieves state-of-the-art performance with sentence representation features only.</abstract>
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%0 Conference Proceedings
%T Metric for Automatic Machine Translation Evaluation based on Universal Sentence Representations
%A Shimanaka, Hiroki
%A Kajiwara, Tomoyuki
%A Komachi, Mamoru
%Y Cordeiro, Silvio Ricardo
%Y Oraby, Shereen
%Y Pavalanathan, Umashanthi
%Y Rim, Kyeongmin
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana, USA
%F shimanaka-etal-2018-metric
%X Sentence representations can capture a wide range of information that cannot be captured by local features based on character or word N-grams. This paper examines the usefulness of universal sentence representations for evaluating the quality of machine translation. Al-though it is difficult to train sentence representations using small-scale translation datasets with manual evaluation, sentence representations trained from large-scale data in other tasks can improve the automatic evaluation of machine translation. Experimental results of the WMT-2016 dataset show that the proposed method achieves state-of-the-art performance with sentence representation features only.
%R 10.18653/v1/N18-4015
%U https://aclanthology.org/N18-4015
%U https://doi.org/10.18653/v1/N18-4015
%P 106-111

Markdown (Informal)

[Metric for Automatic Machine Translation Evaluation based on Universal Sentence Representations](https://aclanthology.org/N18-4015) (Shimanaka et al., NAACL 2018)

• Metric for Automatic Machine Translation Evaluation based on Universal Sentence Representations (Shimanaka et al., NAACL 2018)

ACL

• Hiroki Shimanaka, Tomoyuki Kajiwara, and Mamoru Komachi. 2018. Metric for Automatic Machine Translation Evaluation based on Universal Sentence Representations. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop, pages 106–111, New Orleans, Louisiana, USA. Association for Computational Linguistics.