Analysis of Topic Modeling on Phrase-Based SMT system for English-Hindi Translation

After availability of cheaper large memory and high performance processors, Statistical Machine Translation (SMT) methods have drawn attention of researchers NLP. Phrase-based SMT has shown better results than word-based SMT. To improve performance of machine translation system further, different systems have been developed which use phrase-based SMT as a baseline system. Domain adaptation is one of most popular example of such systems. In this paper also phrase-based SMT system is used as baseline to apply topic model for English-Hindi translation. This baseline system is also used for result comparison with topic model system. Both systems are trained using MERT. The analysis shows improvement in results obtained by using topic modeling system

Passive-aggressive for on-line learning in statistical machine translation

New variations on the application of the passive-aggressive algorithm to statistical machine translation are developed and compared to previously existing approaches. In online adaptation, the system needs to adapt to real-world changing scenarios, where training and tuning only take place when the system is set-up for the first time. Post-edit information, as described by a given quality measure, is used as valuable feedback within the passive-aggressive framework, adapting the statistical models on-line. First, by modifying the translation model parameters, and alternatively, by adapting the scaling factors present in stateof- the-art SMT systems. Experimental results show improvements in translation quality by allowing the system to learn on a sentence-by-sentence basis.This paper is based upon work supported by the EC (FEDER/FSE) and the Spanish MICINN under projects MIPRCV “Consolider Ingenio 2010” (CSD2007-00018) and iTrans2 (TIN2009-14511). Also supported by the Spanish MITyC under the erudito.com (TSI-020110-2009-439) project, by the Generalitat Valenciana under grant Prometeo/2009/014 and scholarship GV/2010/067 and by the UPV under grant 20091027.Martínez Gómez, P.; Sanchis Trilles, G.; Casacuberta Nolla, F. (2011). Passive-aggressive for on-line learning in statistical machine translation. En Pattern Recognition and Image Analysis. Springer Verlag (Germany). 6669:240-247. https://doi.org/10.1007/978-3-642-21257-4_30S2402476669Barrachina, S., et al.: Statistical approaches to computer-assisted translation. Computational Linguistics 35(1), 3–28 (2009)Callison-Burch, C., Bannard, C., Schroeder, J.: Improving statistical translation through editing. In: Proc. of 9th EAMT Workshop Broadening Horizons of Machine Translation and its Applications, Malta (April 2004)Callison-Burch, C., Fordyce, C., Koehn, P., Monz, C., Schroeder, J.: (meta-) evaluation of machine translation. In: Proc. of the Workshop on SMT, pp. 136–158. ACL (June 2007)Crammer, K., Dekel, O., Keshet, J., Shalev-Shwartz, S., Singer, Y.: Online passive-aggressive algorithms. Journal of Machine Learning Research 7, 551–585 (2006)Kneser, R., Ney, H.: Improved backing-off for m-gram language modeling. In: IEEE Int. Conf. on Acoustics, Speech and Signal Processing II, pp. 181–184 (May 1995)Koehn, P.: Europarl: A parallel corpus for statistical machine translation. In: Proc. of the MT Summit X, pp. 79–86 (2005)Koehn, P., et al.: Moses: Open source toolkit for statistical machine translation. In: Proc. of the ACL Demo and Poster Sessions, Prague, Czech Republic, pp. 177–180 (2007)Och, F., Ney, H.: Discriminative training and maximum entropy models for statistical machine translation. In: Proc. of the ACL 2002, pp. 295–302 (2002)Och, F.: Minimum error rate training for statistical machine translation. In: Dignum, F.P.M. (ed.) ACL 2003. LNCS (LNAI), vol. 2922, pp. 160–167. Springer, Heidelberg (2004)Ortiz-Martínez, D., García-Varea, I., Casacuberta, F.: Online learning for interactive statistical machine translation. In: Proceedings of NAACL HLT, Los Angeles (June 2010)Papineni, K., Roukos, S., Ward, T.: Maximum likelihood and discriminative training of direct translation models. In: Proc. of ICASSP 1998, pp. 189–192 (1998)Papineni, K., Roukos, S., Ward, T., Zhu, W.: Bleu: A method for automatic evaluation of machine translation. In: Proc. of ACL 2002, pp. 311–318 (2002)Reverberi, G., Szedmak, S., Cesa-Bianchi, N., et al.: Deliverable of package 4: Online learning algorithms for computer-assisted translation (2008)Sanchis-Trilles, G., Casacuberta, F.: Log-linear weight optimisation via bayesian adaptation in statistical machine translation. In: Proc. of COLING 2010, Beijing, China, pp. 1077–1085 (August 2010)Snover, M., et al.: A study of translation edit rate with targeted human annotation. In: Proc. of AMTA 2006, Cambridge, Massachusetts, USA, pp. 223–231 (August 2006)Zens, R., Och, F., Ney, H.: Phrase-based statistical machine translation. In: Jarke, M., Koehler, J., Lakemeyer, G. (eds.) KI 2002. LNCS (LNAI), vol. 2479, pp. 18–32. Springer, Heidelberg (2002

Domain specific adaptation of a statistical machine translation engine in Slovene language

Machine translation, especially statistical machine translation gained a lot of interest in recent years, mainly thanks to the increase of publicly available multilingual language resources. In terms of obtaining the basic understanding of the target language text, the majority of free machine translation systems give us satisfactory results but are not accurate enough for specific domain texts. For some foreign languages, research shows increases in the quality of the machine translation if trained with the in-domain data. Such research has not yet been conducted for the Slovenian language which presents the motivation for our research. Additional motivation is the nonexistence of a publicly available language model for the Slovenian language. This master thesis focuses on a statistical machine translation system adaptation for a specific domain in the Slovenian language. Various approaches for the adaptation to a specific domain are described. We set up the Moses machine translation system framework and acquire and adapt existing general corpora for the Slovenian language as a basis for building a comparative linguistic model. Annotated and non-annotated Slovenian corpus, ccGigafida, is used to create a linguistic model of the Slovenian language. For the pharmaceutical domain, existing English-Slovenian translations and other linguistic resources have been found and adapted to serve as a learning base for the machine translation system. We evaluate the impact of various linguistic resources on the quality of machine translation for the pharmaceutical domain. The evaluation is conducted automatically using the BLEU metrics. In addition, some test translations are manually evaluated by experts and potential system users. The analysis shows that test translations, translated with the domain model, achieve better results than translations that are generated using the out-of-domain model. Surprisingly, bigger, combined model, does not achieve better results than the smaller domain model. The manual analysis of the resulting fluency and adequacy shows that translations that achieve a high BLEU grade can achieve lower fluency or adequacy grades than the test translations that otherwise achieved a lower BLEU grade. The experiment with the addition of the domain-based dictionary to the in-domain translation model shows a gain of 1 BLEU grade and assures the use of the desired terminology

Domain specific adaptation of a statistical machine translation engine in Slovene language

Machine translation, especially statistical machine translation gained a lot of interest in recent years, mainly thanks to the increase of publicly available multilingual language resources. In terms of obtaining the basic understanding of the target language text, the majority of free machine translation systems give us satisfactory results but are not accurate enough for specific domain texts. For some foreign languages, research shows increases in the quality of the machine translation if trained with the in-domain data. Such research has not yet been conducted for the Slovenian language which presents the motivation for our research. Additional motivation is the nonexistence of a publicly available language model for the Slovenian language. This master thesis focuses on a statistical machine translation system adaptation for a specific domain in the Slovenian language. Various approaches for the adaptation to a specific domain are described. We set up the Moses machine translation system framework and acquire and adapt existing general corpora for the Slovenian language as a basis for building a comparative linguistic model. Annotated and non-annotated Slovenian corpus, ccGigafida, is used to create a linguistic model of the Slovenian language. For the pharmaceutical domain, existing English-Slovenian translations and other linguistic resources have been found and adapted to serve as a learning base for the machine translation system. We evaluate the impact of various linguistic resources on the quality of machine translation for the pharmaceutical domain. The evaluation is conducted automatically using the BLEU metrics. In addition, some test translations are manually evaluated by experts and potential system users. The analysis shows that test translations, translated with the domain model, achieve better results than translations that are generated using the out-of-domain model. Surprisingly, bigger, combined model, does not achieve better results than the smaller domain model. The manual analysis of the resulting fluency and adequacy shows that translations that achieve a high BLEU grade can achieve lower fluency or adequacy grades than the test translations that otherwise achieved a lower BLEU grade. The experiment with the addition of the domain-based dictionary to the in-domain translation model shows a gain of 1 BLEU grade and assures the use of the desired terminology

Experiments on domain adaptation for English-Hindi SMT

Statistical Machine Translation (SMT) systems are usually trained on large amounts of bilingual text and monolingual target language text. If a significant amount of out-of-domain data is added to the training data, the quality of translation can drop. On the other hand, training an SMT system on a small amount of training material for given indomain data leads to narrow lexical coverage which again results in a low translation quality. In this paper, (i) we explore domain-adaptation techniques to combine large out-of-domain training data with small-scale in-domain training data for English—Hindi statistical machine translation and (ii) we cluster large out-of-domain training data to extract sentences similar to in-domain sentences and apply adaptation techniques to combine clustered sub-corpora with in-domain training data into a unified framework, achieving a 0.44 absolute corresponding to a 4.03% relative improvement in terms of BLEU over the baseline

Combining multi-domain statistical machine translation models using automatic classifiers

This paper presents a set of experiments on Domain Adaptation of Statistical Machine Translation systems. The experiments focus on Chinese-English and two domain-specific corpora. The paper presents a novel approach for combining multiple domain-trained translation models to achieve improved translation quality for both domain-specific as well as combined sets of sentences. We train a statistical classifier to classify sentences according to the appropriate domain and utilize the corresponding domain-specific MT models to translate them. Experimental results show that the method achieves a statistically significant absolute improvement of 1.58 BLEU (2.86% relative improvement) score over a translation model trained on combined data, and considerable improvements over a model using multiple decoding paths of the Moses decoder, for the combined domain test set. Furthermore, even for domain-specific test sets, our approach works almost as well as dedicated domain-specific models and perfect classification