5.00
Hdl Handle:
http://hdl.handle.net/2436/620274
Title:
Improving translation memory matching and retrieval using paraphrases
Authors:
Gupta, Rohit ( 0000-0002-5729-1529 ) ; Orasan, Constantin ( 0000-0003-2067-8890 ) ; Zampieri, Marcos; Vela, Mihaela; van Genabith, Josef; Mitkov, Ruslan
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.
Citation:
Improving translation memory matching and retrieval using paraphrases 2016 Machine Translation
Publisher:
Springer Nature
Journal:
Machine Translation
Issue Date:
2-Nov-2016
URI:
http://hdl.handle.net/2436/620274
DOI:
10.1007/s10590-016-9180-0
Additional Links:
http://link.springer.com/10.1007/s10590-016-9180-0
Type:
Article
Language:
en
ISSN:
0922-6567
Appears in Collections:
Computational Linguistics Group

Full metadata record

DC FieldValue Language
dc.contributor.authorGupta, Rohiten
dc.contributor.authorOrasan, Constantinen
dc.contributor.authorZampieri, Marcosen
dc.contributor.authorVela, Mihaelaen
dc.contributor.authorvan Genabith, Josefen
dc.contributor.authorMitkov, Ruslanen
dc.date.accessioned2016-11-09T16:12:24Z-
dc.date.available2016-11-09T16:12:24Z-
dc.date.issued2016-11-02-
dc.identifier.citationImproving translation memory matching and retrieval using paraphrases 2016 Machine Translationen
dc.identifier.issn0922-6567en
dc.identifier.doi10.1007/s10590-016-9180-0-
dc.identifier.urihttp://hdl.handle.net/2436/620274-
dc.description.abstractMost 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.en
dc.language.isoenen
dc.publisherSpringer Natureen
dc.relation.urlhttp://link.springer.com/10.1007/s10590-016-9180-0en
dc.rightsArchived with thanks to Machine Translationen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectTranslation memory (TM)en
dc.subjectParaphrasingen
dc.subjectComputer aided translation (CAT)en
dc.subjectEdit distanceen
dc.subjectDynamic programmingen
dc.subjectGreedy approximationen
dc.titleImproving translation memory matching and retrieval using paraphrasesen
dc.typeArticleen
dc.identifier.journalMachine Translationen
dc.date.accepted2016-10-
rioxxterms.funderECen
rioxxterms.identifier.projectFP7 ITN People's Programme #317471en
rioxxterms.versionAMen
rioxxterms.licenseref.urihttps://creativecommons.org/CC BY-NC-ND 4.0en
rioxxterms.licenseref.startdate2017-11-01en
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