Abstract
Translation memory tools lack semantic knowledge like paraphrasing when they perform matching and retrieval. As a result, paraphrased segments are often not retrieved. One of the primary reasons for this is the lack of a simple and efficient algorithm to incorporate paraphrasing in the TM matching process. Gupta and Orăsan [1] proposed an algorithm which incorporates paraphrasing based on greedy approximation and dynamic programming. However, because of greedy approximation, their approach does not make full use of the paraphrases available. In this paper we propose an efficient method for incorporating paraphrasing in matching and retrieval based on dynamic programming only. We tested our approach on English-German, English-Spanish and English-French language pairs and retrieved better results for all three language pairs compared to the earlier approach [1].
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Notes
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- 2.
We have used Stanford tokenizer on the English side and tokenizer provided with Moses [15] on the target side. The source (English) tokenization is used for matching and target language tokenization is used when calculating BLEU score.
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Acknowledgement
The research leading to these results has received funding from the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme FP7/2007-2013/ under REA grant agreement No. 317471.
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Gupta, R., Orăsan, C., Liu, Q., Mitkov, R. (2016). A Dynamic Programming Approach to Improving Translation Memory Matching and Retrieval Using Paraphrases. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds) Text, Speech, and Dialogue. TSD 2016. Lecture Notes in Computer Science(), vol 9924. Springer, Cham. https://doi.org/10.1007/978-3-319-45510-5_30
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