Publikation
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), Vol. 30, No. 1-2, Pages 19-40, Springer, 2016.
Zusammenfassung
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.