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Segment-based interactive-predictive machine translation

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Segment-based interactive-predictive machine translation

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dc.contributor.author Domingo-Ballester, Miguel es_ES
dc.contributor.author Peris-Abril, Álvaro es_ES
dc.contributor.author Casacuberta Nolla, Francisco es_ES
dc.date.accessioned 2018-06-08T04:34:27Z
dc.date.available 2018-06-08T04:34:27Z
dc.date.issued 2017 es_ES
dc.identifier.issn 0922-6567 es_ES
dc.identifier.uri http://hdl.handle.net/10251/103640
dc.description.abstract [EN] Machine translation systems require human revision to obtain high-quality translations. Interactive methods provide an efficient human¿computer collaboration, notably increasing productivity. Recently, new interactive protocols have been proposed, seeking for a more effective user interaction with the system. In this work, we present one of these new protocols, which allows the user to validate all correct word sequences in a translation hypothesis. Thus, the left-to-right barrier from most of the existing protocols is broken. We compare this protocol against the classical prefix-based approach, obtaining a significant reduction of the user effort in a simulated environment. Additionally, we experiment with the use of confidence measures to select the word the user should correct at each iteration, reaching the conclusion that the order in which words are corrected does not affect the overall effort. es_ES
dc.description.sponsorship The research leading to these results has received funding from the Ministerio de Economia y Competitividad (MINECO) under Project CoMUN-HaT (Grant Agreement TIN2015-70924-C2-1-R), and Generalitat Valenciana under Project ALMAMATER (Ggrant Agreement PROMETEOII/2014/030). en_EN
dc.language Inglés es_ES
dc.publisher Springer-Verlag es_ES
dc.relation.ispartof Machine Translation es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Machine translation es_ES
dc.subject Computer-assisted translation es_ES
dc.subject Interactive-predictive machine translation es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title Segment-based interactive-predictive machine translation es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s10590-017-9213-3 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//TIN2015-70924-C2-1-R/ES/CONTEXTO, MULTIMODALIDAD Y COLABORACION DEL USUARIO EN PROCESADO DE TEXTO MANUSCRITO/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/GVA//PROMETEOII%2F2014%2F030/ES/ Adaptive learning and multimodality in machine translation and text transcription/ es_ES
dc.rights.accessRights Abierto es_ES
dc.date.embargoEndDate 2019-01-05 es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.description.bibliographicCitation Domingo-Ballester, M.; Peris-Abril, Á.; Casacuberta Nolla, F. (2017). Segment-based interactive-predictive machine translation. Machine Translation. 31(4):163-185. https://doi.org/10.1007/s10590-017-9213-3 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1007/s10590-017-9213-3 es_ES
dc.description.upvformatpinicio 163 es_ES
dc.description.upvformatpfin 185 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 31 es_ES
dc.description.issue 4 es_ES
dc.relation.pasarela S\353527 es_ES
dc.contributor.funder Generalitat Valenciana es_ES
dc.contributor.funder Ministerio de Economía, Industria y Competitividad es_ES
dc.description.references Alabau V, Bonk R, Buck C, Carl M, Casacuberta F, García-Martínez M, González-Rubio J, Koehn P, Leiva LA, Mesa-Lao B, Ortiz-Martínez D, Saint-Amand H, Sanchis-Trilles G, Tsoukala C (2013) CASMACAT: an open source workbench for advanced computer aided translation. Prague Bull Math Linguist 100:101–112 es_ES
dc.description.references Alabau V, Rodríguez-Ruiz L, Sanchis A, Martínez-Gómez P, Casacuberta F (2011) On multimodal interactive machine translation using speech recognition. In: Proceedings of the International Conference on Multimodal Interaction, pp 129–136 es_ES
dc.description.references Alabau V, Sanchis A, Casacuberta F (2014) Improving on-line handwritten recognition in interactive machine translation. Pattern Recognit 47(3):1217–1228 es_ES
dc.description.references Apostolico A, Guerra C (1987) The longest common subsequence problem revisited. Algorithmica 2:315–336 es_ES
dc.description.references Azadi F, Khadivi S (2015) Improved search strategy for interactive machine translation in computer assisted translation. In: Proceedings of Machine Translation Summit XV, pp 319–332 es_ES
dc.description.references Bahdanau D, Cho K, Bengio Y (2015) Neural machine translation by jointly learning to align and translate. In: Proceedings of the International Conference on Learning Representations. arXiv:1409.0473 es_ES
dc.description.references Barrachina S, Bender O, Casacuberta F, Civera J, Cubel E, Khadivi S, Lagarda A, Ney H, Tomás J, Vidal E, Vilar J-M (2009) Statistical approaches to computer-assisted translation. Comput Linguist 35:3–28 es_ES
dc.description.references Brown PF, Pietra VJD, Pietra SAD, Mercer RL (1993) The mathematics of statistical machine translation: parameter estimation. Comput Linguist 19(2):263–311 es_ES
dc.description.references Chen SF, Goodman J (1996) An empirical study of smoothing techniques for language modeling. In: Proceedings of the Annual Meeting on Association for Computational Linguistics, pp 310–318 es_ES
dc.description.references Cheng S, Huang S, Chen H, Dai X, Chen J (2016) PRIMT: a pick-revise framework for interactive machine translation. In: Proceedings of the North American Chapter of the Association for Computational Linguistics, pp 1240–1249 es_ES
dc.description.references Dale R (2016) How to make money in the translation business. Nat Lang Eng 22(2):321–325 es_ES
dc.description.references Domingo M, Peris, Á, Casacuberta F (2016) Interactive-predictive translation based on multiple word-segments. In: Proceedings of the Annual Conference of the European Association for Machine Translation, pp 282–291 es_ES
dc.description.references Federico M, Bentivogli L, Paul M, Stüker S (2011) Overview of the IWSLT 2011 evaluation campaign. In: International Workshop on Spoken Language Translation, pp 11–27 es_ES
dc.description.references Foster G, Isabelle P, Plamondon P (1997) Target-text mediated interactive machine translation. Mach Transl 12:175–194 es_ES
dc.description.references González-Rubio J, Benedí J-M, Casacuberta F (2016) Beyond prefix-based interactive translation prediction. In: Proceedings of the SIGNLL Conference on Computational Natural Language Learning, pp 198–207 es_ES
dc.description.references González-Rubio J, Ortiz-Martínez D, Casacuberta F (2010) On the use of confidence measures within an interactive-predictive machine translation system. In: Proceedings of the Annual Conference of the European Association for Machine Translation es_ES
dc.description.references Knowles R, Koehn P (2016) Neural interactive translation prediction. In: Proceedings of the Association for Machine Translation in the Americas, pp 107–120 es_ES
dc.description.references Koehn P (2005) Europarl: a parallel corpus for statistical machine translation. In: Proceedings of the Machine Translation Summit, pp 79–86 es_ES
dc.description.references Koehn P (2010) Statistical machine translation. Cambridge University Press, Cambridge es_ES
dc.description.references Koehn P, Hoang H, Birch A, Callison-Burch C, Federico M, Bertoldi N, Cowan B, Shen W, Moran C, Zens R, Dyer C, Bojar O, Constantin A, Herbst E (2007) Moses: open source toolkit for statistical machine translation. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics, pp 177–180 es_ES
dc.description.references Koehn P, Och FJ, Marcu D (2003) Statistical phrase-based translation. In: Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, pp 48–54 es_ES
dc.description.references Koehn P, Tsoukala C, Saint-Amand H (2014) Refinements to interactive translation prediction based on search graphs. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics, pp 574–578 es_ES
dc.description.references Marie B, Max A (2015) Touch-based pre-post-editing of machine translation output. In: Proceedings of the conference on empirical methods in natural language processing, pp 1040–1045 es_ES
dc.description.references Nepveu L, Lapalme G, Langlais P, Foster G (2004) Adaptive language and translation models for interactive machine translation. In: Proceedings of the conference on empirical method in natural language processing, pp 190–197 es_ES
dc.description.references Nielsen J (1993) Usability engineering. Morgan Kaufmann Publishers Inc, Burlington es_ES
dc.description.references Och F J (2003) Minimum error rate training in statistical machine translation. In: Proceedings of the annual meeting of the association for computational linguistics, pp 160–167 es_ES
dc.description.references Och FJ, Ney H (2002) Discriminative training and maximum entropy models for statistical machine translation. In: Proceedings of the annual meeting of the association for computational linguistics, pp 295–302 es_ES
dc.description.references Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Comput Linguist 29(1):19–51 es_ES
dc.description.references Ortiz-Martínez D (2016) Online learning for statistical machine translation. Comput Linguist 42(1):121–161 es_ES
dc.description.references Papineni K, Roukos S, Ward T, Zhu W-J (2002) BLEU: a method for automatic evaluation of machine translation. In: Proceedings of the annual meeting of the association for computational linguistics, pp 311–318 es_ES
dc.description.references Peris Á, Domingo M, Casacuberta F (2017) Interactive neural machine translation. Comput Speech Lang. 45:201–220 es_ES
dc.description.references Sanchis-Trilles G, Ortiz-Martínez D, Civera J, Casacuberta F, Vidal E, Hoang H (2008) Improving interactive machine translation via mouse actions. In: Proceedings of the conference on empirical methods in natural language processing, pp 485–494 es_ES
dc.description.references Snover M, Dorr B, Schwartz R, Micciulla L, Makhoul J (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the Association for Machine Translation in the Americas, pp 223–231 es_ES
dc.description.references Stolcke A (2002) SRILM—an extensible language modeling toolkit. In: Proceedings of the international conference on spoken language processing, pp 257–286 es_ES
dc.description.references Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. NIPS 27:3104–3112 es_ES
dc.description.references Tiedemann J (2009) News from OPUS—a collection of multilingual parallel corpora with tools and interfaces. Recent Adv Nat Lang Process 5:237–248 es_ES
dc.description.references Tomás J, Casacuberta F(2006) Statistical phrase-based models for interactive computer-assisted translation. In: Proceedings of the international conference on computational linguistics/Association for Computational Linguistics, pp 835–841 es_ES
dc.description.references Torregrosa D, Forcada ML, Pérez-Ortiz JA (2014) An open-source web-based tool for resource-agnostic interactive translation prediction. Prague Bull Math Linguist 102:69–80 es_ES
dc.description.references Tseng H, Chang P, Andrew G, Jurafsky D, Manning C (2005) A conditional random field word segmenter. In: Proceedings of the special interest group of the association for computational linguistics workshop on Chinese language processing, pp 168–171 es_ES
dc.description.references Ueffing N, Ney H (2005) Application of word-level confidence measures in interactive statistical machine translation. In: Proceedings of the European Association for Machine Translation, pp 262–270 es_ES
dc.description.references Vogel S, Ney H, Tillmann C (1996) HMM-based word alignment in statistical translation. Proc Conf Comput Linguist 2:836–841 es_ES
dc.description.references Wuebker J, Green S, DeNero J, Hasan S, Luong M-T(2016) Models and inference for prefix-constrained machine translation. In: Proceedings of the annual meeting of the association for the computational linguistics, pp 66–75 es_ES
dc.description.references Zens R, Och FJ, Ney H (2002) Phrase-based statistical machine translation. In: Proceedings of the annual German conference on advances in artificial intelligence 2479:18–32 es_ES


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