Publication

Improving Translation Memory Matching and Retrieval Using Paraphrases.

Rohit Gupta, Constantin Orăsan, Marcos Zampieri, Mihaela Vela, Josef van Genabith, Ruslan Mitkov

In: Machine Translation (MT) 30 1-2 Pages 19-40 Springer 2016.

Abstract

Most current translation memory (TM) systems work on the string level (character or word level) and lack semantic knowledge while matching. They use simple edit-distance (ED) calculated on the 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 (PP) in the ED metric. The approach computes ED 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 PP substantially improves TMs.

Weitere Links

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz