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Log-Linear Weight Optimization Using Discriminative Ridge Regression Method in Statistical Machine Translation

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Log-Linear Weight Optimization Using Discriminative Ridge Regression Method in Statistical Machine Translation

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dc.contributor.author Chinea-Ríos, Mara es_ES
dc.contributor.author Sanchis Trilles, Germán es_ES
dc.contributor.author Casacuberta Nolla, Francisco es_ES
dc.date.accessioned 2018-06-08T04:23:19Z
dc.date.available 2018-06-08T04:23:19Z
dc.date.issued 2017 es_ES
dc.identifier.issn 0302-9743 es_ES
dc.identifier.uri http://hdl.handle.net/10251/103609
dc.description.abstract [EN] We present a simple and reliable method for estimating the log-linear weights of a state-of-the-art machine translation system, which takes advantage of the method known as discriminative ridge regression (DRR). Since inappropriate weight estimations lead to a wide variability of translation quality results, reaching a reliable estimate for such weights is critical for machine translation research. For this reason, a variety of methods have been proposed to reach reasonable estimates. In this paper, we present an algorithmic description and empirical results proving that DRR, as applied in a pseudo-batch scenario, is able to provide comparable translation quality when compared to state-of-the-art estimation methods (i.e., MERT [1] and MIRA [2]). Moreover, the empirical results reported are coherent across different corpora and language pairs. es_ES
dc.description.sponsorship The research leading to these results has received funding fromthe Generalitat Valenciana under grant PROMETEOII/2014/030 and the FPI (2014) grant by Universitat Politècnica de València. es_ES
dc.language Inglés es_ES
dc.publisher Springer-Verlag es_ES
dc.relation.ispartof Lecture Notes in Computer Science es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Statistical machine translation es_ES
dc.subject Log-linear model es_ES
dc.subject Discriminative ridge regression es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title Log-Linear Weight Optimization Using Discriminative Ridge Regression Method in Statistical Machine Translation es_ES
dc.type Artículo es_ES
dc.type Comunicación en congreso es_ES
dc.identifier.doi 10.1007/978-3-319-58838-4_4 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.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.description.bibliographicCitation Chinea-Ríos, M.; Sanchis Trilles, G.; Casacuberta Nolla, F. (2017). Log-Linear Weight Optimization Using Discriminative Ridge Regression Method in Statistical Machine Translation. Lecture Notes in Computer Science. 10255:32-41. https://doi.org/10.1007/978-3-319-58838-4_4 es_ES
dc.description.accrualMethod S es_ES
dc.relation.conferencename 8th Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2017) es_ES
dc.relation.conferencedate June 20-23,2017 es_ES
dc.relation.conferenceplace Faro, Portugal es_ES
dc.relation.publisherversion https://doi.org/10.1007/978-3-319-58838-4_4 es_ES
dc.description.upvformatpinicio 32 es_ES
dc.description.upvformatpfin 41 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 10255 es_ES
dc.relation.pasarela S\342410 es_ES
dc.contributor.funder Generalitat Valenciana es_ES
dc.contributor.funder Universitat Politècnica de València es_ES
dc.description.references Och, F.J.: Minimum error rate training in statistical machine translation. In: Proceedings of ACL, pp. 160–167 (2003) es_ES
dc.description.references Crammer, K., Dekel, O., Keshet, J., Shalev-Shwartz, S., Singer, Y.: Online passive-aggressive algorithms. J. Mach. Learn. Res. 7, 551–585 (2006) es_ES
dc.description.references Och, F.J., Ney, H.: A systematic comparison of various statistical alignment models. Comput. Linguist. 29, 19–51 (2003) es_ES
dc.description.references Koehn, P.: Statistical Machine Translation. Cambridge University Press, Cambridge (2010) es_ES
dc.description.references Martínez-Gómez, P., Sanchis-Trilles, G., Casacuberta, F.: Online adaptation strategies for statistical machine translation in post-editing scenarios. Pattern Recogn. 45(9), 3193–3203 (2012) es_ES
dc.description.references Cherry, C., Foster, G.: Batch tuning strategies for statistical machine translation. In: Proceedings of NAACL, pp. 427–436 (2012) es_ES
dc.description.references Sanchis-Trilles, G., Casacuberta, F.: Log-linear weight optimisation via Bayesian adaptation in statistical machine translation. In: Proceedings of ACL, pp. 1077–1085 (2010) es_ES
dc.description.references Marie, B., Max, A.: Multi-pass decoding with complex feature guidance for statistical machine translation. In: Proceedings of ACL, pp. 554–559 (2015) es_ES
dc.description.references Hopkins, M., May, J.: Tuning as ranking. In: Proceedings of EMNLP, pp. 1352–1362 (2011) es_ES
dc.description.references Stauffer, C., Grimson, W.E.L.: Learning patterns of activity using real-time tracking. Pattern Anal. Mach. Intell. 22(8), 747–757 (2000) 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.: Moses: open source toolkit for statistical machine translation. In: Proceedings of ACL, pp. 177–180 (2007) es_ES
dc.description.references Kneser, R., Ney, H.: Improved backing-off for m-gram language modeling. In: Proceedings of ICASSP, pp. 181–184 (1995) es_ES
dc.description.references Stolcke, A.: Srilm-an extensible language modeling toolkit. In: Proceedings of ICSLP, pp. 901–904 (2002) es_ES
dc.description.references Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: BLEU: a method for automatic evaluation of machine translation. In: Proceedings of ACL, pp. 311–318 (2002) es_ES
dc.description.references Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level BLEU. In: Proceedings of WMT, pp. 362–367 (2014) es_ES
dc.description.references Snover, M., Dorr, B.J., Schwartz, R., Micciulla, L., Makhoul, J.: A study of translation edit rate with targeted human annotation. In: Proceedings of AMTA, pp. 223–231 (2006) es_ES
dc.description.references Tiedemann, J.: News from opus-a collection of multilingual parallel corpora with tools and interfaces. In: Proceedings of RANLP, pp. 237–248 (2009) es_ES
dc.description.references Tiedemann, J.: Parallel data, tools and interfaces in opus. In: Proceedings of LREC, pp. 2214–2218 (2012) es_ES


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