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        <title>Meteor++: Incorporating Copy Knowledge into Machine Translation Evaluation</title>
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        <namePart type="given">Yinuo</namePart>
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    <abstract>In machine translation evaluation, a good candidate translation can be regarded as a paraphrase of the reference. We notice that some words are always copied during paraphrasing, which we call copy knowledge. Considering the stability of such knowledge, a good candidate translation should contain all these words appeared in the reference sentence. Therefore, in this participation of the WMT’2018 metrics shared task we introduce a simple statistical method for copy knowledge extraction, and incorporate it into Meteor metric, resulting in a new machine translation metric Meteor++. Our experiments show that Meteor++ can nicely integrate copy knowledge and improve the performance significantly on WMT17 and WMT15 evaluation sets.</abstract>
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    <identifier type="doi">10.18653/v1/W18-6454</identifier>
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        <date>2018-oct</date>
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%0 Conference Proceedings
%T Meteor++: Incorporating Copy Knowledge into Machine Translation Evaluation
%A Guo, Yinuo
%A Ruan, Chong
%A Hu, Junfeng
%S Proceedings of the Third Conference on Machine Translation: Shared Task Papers
%D 2018
%8 oct
%I Association for Computational Linguistics
%C Belgium, Brussels
%F guo-etal-2018-meteor
%X In machine translation evaluation, a good candidate translation can be regarded as a paraphrase of the reference. We notice that some words are always copied during paraphrasing, which we call copy knowledge. Considering the stability of such knowledge, a good candidate translation should contain all these words appeared in the reference sentence. Therefore, in this participation of the WMT’2018 metrics shared task we introduce a simple statistical method for copy knowledge extraction, and incorporate it into Meteor metric, resulting in a new machine translation metric Meteor++. Our experiments show that Meteor++ can nicely integrate copy knowledge and improve the performance significantly on WMT17 and WMT15 evaluation sets.
%R 10.18653/v1/W18-6454
%U https://aclanthology.org/W18-6454
%U https://doi.org/10.18653/v1/W18-6454
%P 740-745

Markdown (Informal)

[Meteor++: Incorporating Copy Knowledge into Machine Translation Evaluation](https://aclanthology.org/W18-6454) (Guo et al., 2018)

• Meteor++: Incorporating Copy Knowledge into Machine Translation Evaluation (Guo et al., 2018)

ACL

• Yinuo Guo, Chong Ruan, and Junfeng Hu. 2018. Meteor++: Incorporating Copy Knowledge into Machine Translation Evaluation. In Proceedings of the Third Conference on Machine Translation: Shared Task Papers, pages 740–745, Belgium, Brussels. Association for Computational Linguistics.