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Improving translation memory matching and retrieval using paraphrases
Gupta, Rohit Orasan, Constantin Zampieri, Marcos Vela, Mihaela van Genabith, Josef Mitkov, Ruslan
Gupta, Rohit
Orasan, Constantin
Zampieri, Marcos
Vela, Mihaela
van Genabith, Josef
Mitkov, Ruslan
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2016-11-02
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TMwithPP15Sep16.pdf
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Abstract
Most of the current Translation Memory (TM) systems work on string level (character or word level) and lack semantic knowledge while matching. They use simple edit-distance calculated on surface-form or some variation on it (stem, lemma), which does not take into consideration any semantic aspects in matching. This paper presents a novel and efficient approach to incorporating semantic information in the form of paraphrasing in the edit-distance metric. The approach computes edit-distance while efficiently considering paraphrases using dynamic programming and greedy approximation. In addition to using automatic evaluation metrics like BLEU and METEOR, we have carried out an extensive human evaluation in which we measured post-editing time, keystrokes, HTER, HMETEOR, and carried out three rounds of subjective evaluations. Our results show that paraphrasing substantially improves TM matching and retrieval, resulting in translation performance increases when translators use paraphrase-enhanced TMs.
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Gupta, R., Orăsan, C., Zampieri, M. et al. (2016) Improving translation memory matching and retrieval using paraphrases. Machine Translation 30 (1), pp 19–40.
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Journal article
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en
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This is an accepted manuscript of an article published by Springer Nature in Machine Translation on 02/11/2016, available online: https://doi.org/10.1007/s10590-016-9180-0
The accepted version of the publication may differ from the final published version.
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0922-6567
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