Bilingual Word Spectral Clustering for Statistical Machine Translation

In this paper, a variant of a spectral clustering algorithm is proposed for bilingual word clustering. The proposed algorithm generates the two sets of clusters for both languages efficiently with high semantic correlation within monolingual clusters, and high translation quality across the clusters between two languages. Each cluster level translation is considered as a bilingual concept, which generalizes words in bilingual clusters. This scheme improves the robustness for statistical machine translation models. Two HMM-based translation models are tested to use these bilingual clusters. Improved perplexity, word alignment accuracy, and translation quality are observed in our experiments

Developing Word-aligned Myanmar-English Parallel Corpus based on the IBM Models

Word alignment in bilingual corpora has been an active research topic in the Machine Translation research groups. Corpus is the body of text collections, which are useful for Language Processing (NLP). Parallel text alignment is the identification of the corresponding sentences in the parallel text. Large collections of parallel level are prerequisite for many areas of linguistic research. Parallel corpus helps in making statistical bilingual dictionary, in supporting statistical machine translation and in supporting as training data for word sense disambiguation and translation disambiguation. Nowadays, the world is a global network and everybody will be learned more than one language. So, multilingual corpora are more processing. Thus, the main purpose of this system is to construct word-aligned parallel corpus to be able in Myanmar-English machine translation. One useful concept is to identify correspondences between words in one language and in other language. The proposed approach is based on the first three IBM models and EM algorithm. It also shows that the approach can also be improved by using a list of cognates and morphological analysis

Learning a Log-Linear Model with Bilingual Phrase-Pair Features for Statistical Machine Translation

We propose a set of informative feature functions togheter with a log-linear model framework for bilingual phrase-pair extraction to improve phrase-based statistical machine translation. The base feature functions investigated are phrase length model, phrase-level centers\u27 distortion, lexicon translation equivalence, bracketing constraints and word alignment links. Two generative models show strong baselines withe these base features, illustrating the effectiveness of the proposed feature functions. Strategies of extending the features and a log-linear model of them are proposed to effectively extract phrase-pars from parallel data. Experimental results of TIDES\u2703 Chinese-English small data track show improved translation qualities

GREAT: open source software for statistical machine translation

The final publication is available at Springer via http://dx.doi.org/10.1007/s10590-011-9097-6[EN] In this article, the first public release of GREAT as an open-source, statistical machine translation (SMT) software toolkit is described. GREAT is based on a bilingual language modelling approach for SMT, which is so far implemented for n-gram models based on the framework of stochastic finite-state transducers. The use of finite-state models is motivated by their simplicity, their versatility, and the fact that they present a lower computational cost, if compared with other more expressive models. Moreover, if translation is assumed to be a subsequential process, finite-state models are enough for modelling the existing relations between a source and a target language. GREAT includes some characteristics usually present in state-of-the-art SMT, such as phrase-based translation models or a log-linear framework for local features. Experimental results on a well-known corpus such as Europarl are reported in order to validate this software. A competitive translation quality is achieved, yet using both a lower number of model parameters and a lower response time than the widely-used, state-of-the-art SMT system Moses. © 2011 Springer Science+Business Media B.V.Study was supported by the EC (FEDER, FSE), the Spanish government (MICINN, MITyC, “Plan E”, under Grants MIPRCV “Consolider Ingenio 2010”, iTrans2 TIN2009-14511, and erudito.com TSI-020110-2009-439), and the Generalitat Valenciana (Grant Prometeo/2009/014).González Mollá, J.; Casacuberta Nolla, F. (2011). GREAT: open source software for statistical machine translation. Machine Translation. 25(2):145-160. https://doi.org/10.1007/s10590-011-9097-6S145160252Amengual JC, Benedí JM, Casacuberta F, Castaño MA, Castellanos A, Jiménez VM, Llorens D, Marzal A, Pastor M, Prat F, Vidal E, Vilar JM (2000) The EUTRANS-I speech translation system. 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PhD thesis, Universitat Politècnica de València. Advisor: Casacuberta FGonzález J, Casacuberta F (2009) GREAT: a finite-state machine translation toolkit implementing a grammatical inference approach for transducer inference (GIATI). In: Proceedings of the EACL Workshop on Computational Linguistic Aspects of Grammatical Inference, Athens, Greece, pp 24–32Kanthak S, Vilar D, Matusov E, Zens R, Ney H (2005) Novel reordering approaches in phrase-based statistical machine translation. In: Proceedings of the ACL Workshop on Building and Using Parallel Texts: Data-Driven Machine Translation and Beyond, Ann Arbor, MI, pp 167–174Karttunen L (2001) Applications of finite-state transducers in natural language processing. In: Proceedings of the 5th Conference on Implementation and Application of Automata, London, UK, pp 34–46Kneser R, Ney H (1995) Improved backing-off for n-gram language modeling. 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Segment-based interactive-predictive machine translation

[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.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).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/s10590-017-9213-3S163185314Alabau 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. 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Log-Linear Weight Optimization Using Discriminative Ridge Regression Method in Statistical Machine Translation

[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.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.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. doi:10.1007/978-3-319-58838-4_4S324110255Och, F.J.: Minimum error rate training in statistical machine translation. In: Proceedings of ACL, pp. 160–167 (2003)Crammer, K., Dekel, O., Keshet, J., Shalev-Shwartz, S., Singer, Y.: Online passive-aggressive algorithms. J. Mach. Learn. Res. 7, 551–585 (2006)Och, F.J., Ney, H.: A systematic comparison of various statistical alignment models. Comput. Linguist. 29, 19–51 (2003)Koehn, P.: Statistical Machine Translation. Cambridge University Press, Cambridge (2010)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)Cherry, C., Foster, G.: Batch tuning strategies for statistical machine translation. In: Proceedings of NAACL, pp. 427–436 (2012)Sanchis-Trilles, G., Casacuberta, F.: Log-linear weight optimisation via Bayesian adaptation in statistical machine translation. In: Proceedings of ACL, pp. 1077–1085 (2010)Marie, B., Max, A.: Multi-pass decoding with complex feature guidance for statistical machine translation. In: Proceedings of ACL, pp. 554–559 (2015)Hopkins, M., May, J.: Tuning as ranking. In: Proceedings of EMNLP, pp. 1352–1362 (2011)Stauffer, C., Grimson, W.E.L.: Learning patterns of activity using real-time tracking. Pattern Anal. Mach. Intell. 22(8), 747–757 (2000)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)Kneser, R., Ney, H.: Improved backing-off for m-gram language modeling. In: Proceedings of ICASSP, pp. 181–184 (1995)Stolcke, A.: Srilm-an extensible language modeling toolkit. In: Proceedings of ICSLP, pp. 901–904 (2002)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)Chen, B., Cherry, C.: A systematic comparison of smoothing techniques for sentence-level BLEU. In: Proceedings of WMT, pp. 362–367 (2014)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)Tiedemann, J.: News from opus-a collection of multilingual parallel corpora with tools and interfaces. In: Proceedings of RANLP, pp. 237–248 (2009)Tiedemann, J.: Parallel data, tools and interfaces in opus. In: Proceedings of LREC, pp. 2214–2218 (2012

Discriminative ridge regression algorithm for adaptation in statistical machine translation

[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 is able to provide comparable translation quality when compared to state-of-the-art estimation methods [i.e. MERT and MIRA], with a reduction in computational cost. Moreover, the empirical results reported are coherent across different corpora and language pairs.The research leading to these results were partially supported by projects CoMUN-HaT-TIN2015-70924-C2-1-R (MINECO/FEDER) and PROMETEO/2018/004. We also acknowledge NVIDIA for the donation of a GPU used in this work.Chinea-Ríos, M.; Sanchis-Trilles, G.; Casacuberta Nolla, F. (2019). Discriminative ridge regression algorithm for adaptation in statistical machine translation. Pattern Analysis and Applications. 22(4):1293-1305. https://doi.org/10.1007/s10044-018-0720-5S12931305224Barrachina S, Bender O, Casacuberta F, Civera J, Cubel E, Khadivi S, Lagarda A, Ney H, Tomás J, Vidal E et al (2009) Statistical approaches to computer-assisted translation. Comput Ling 35(1):3–28Bojar O, Buck C, Federmann C, Haddow B, Koehn P, Monz C, Post M, Specia L (eds) (2014) Proceedings of the ninth workshop on statistical machine translation. Association for Computational LinguisticsBrown PF, Pietra VJD, Pietra SAD, Mercer RL (1993) The mathematics of statistical machine translation: parameter estimation. Comput Ling 19:263–311Callison-Burch C, Koehn P, Monz C, Peterson K, Przybocki M, Zaidan OF (2010) Findings of the 2010 joint workshop on statistical machine translation and metrics for machine translation. In: Proceedings of the annual meeting of the association for computational linguistics, pp 17–53Chen B, Cherry C (2014) A systematic comparison of smoothing techniques for sentence-level bleu. In: Proceedings of the workshop on statistical machine translation, pp 362–367Cherry C, Foster G (2012) Batch tuning strategies for statistical machine translation. In: Proceedings of the North American chapter of the association for computational linguistics, pp 427–436Clark JH, Dyer C, Lavie A, Smith NA (2011) Better hypothesis testing for statistical machine translation: controlling for optimizer instability. In: Proceedings of the annual meeting of the association for computational linguistics, pp 176–181Crammer K, Dekel O, Keshet J, Shalev-Shwartz S, Singer Y (2006) Online passive-aggressive algorithms. J Mach Learn Res 7:551–585Hasler E, Haddow B, Koehn P (2011) Margin infused relaxed algorithm for moses. Prague Bull Math Ling 96:69–78Hopkins M, May J (2011) Tuning as ranking. In: Proceedings of the conference on empirical methods in natural language processing, pp 1352–1362Kneser R, Ney H (1995) Improved backing-off for m-gram language modeling. In: Proceedings of the international conference on acoustics, speech and signal processing, pp 181–184Koehn P (2005) Europarl: a parallel corpus for statistical machine translation. In: Proceedings of the machine translation summit, pp 79–86Koehn P (2010) Statistical machine translation. Cambridge University Press, CambridgeKoehn 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 177–180Lavie MDA (2014) Meteor universal: language specific translation evaluation for any target language. In: Proceedings of the annual meeting of the association for computational linguistics, pp 376–387Marie B, Max A (2015) Multi-pass decoding with complex feature guidance for statistical machine translation. In: Proceedings of the annual meeting of the association for computational linguistics, pp 554–559Martínez-Gómez P, Sanchis-Trilles G, Casacuberta F (2012) Online adaptation strategies for statistical machine translation in post-editing scenarios. 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In: Proceedings of the annual meeting of the association for computational linguistics, pp 1077–1085Sanchis-Trilles G, Casacuberta F (2015) Improving translation quality stability using Bayesian predictive adaptation. Comput Speech Lang 34(1):1–17Snover M, Dorr B, Schwartz R, Micciulla L, Makhoul J (2006) A study of translation edit rate with targeted human annotation. In: Proceedings of the annual meeting of the association for machine translation in the Americas, pp 223–231Sokolov A, Yvon F (2011) Minimum error rate training semiring. In: Proceedings of the annual conference of the European association for machine translation, pp 241–248Stauffer C, Grimson WEL (2000) Learning patterns of activity using real-time tracking. Pattern Anal Mach Intell 22(8):747–757Stolcke A (2002) Srilm—an extensible language modeling toolkit. In: Proceedings of the international conference on spoken language processing, pp 901–904Tiedemann J (2009) News from opus—a collection of multilingual parallel corpora with tools and interfaces. In: Proceedings of the recent advances in natural language processing, pp 237–248Tiedemann J (2012) Parallel data, tools and interfaces in opus. In: Proceedings of the language resources and evaluation conference, pp 2214–221

Parallel Treebanks in Phrase-Based Statistical Machine Translation

Given much recent discussion and the shift in focus of the field, it is becoming apparent that the incorporation of syntax is the way forward for the current state-of-the-art in machine translation (MT). Parallel treebanks are a relatively recent innovation and appear to be ideal candidates for MT training material. However, until recently there has been no other means to build them than by hand. In this paper, we describe how we make use of new tools to automatically build a large parallel treebank and extract a set of linguistically motivated phrase pairs from it. We show that adding these phrase pairs to the translation model of a baseline phrase-based statistical MT (PBSMT) system leads to significant improvements in translation quality. We describe further experiments on incorporating parallel treebank information into PBSMT, such as word alignments. We investigate the conditions under which the incorporation of parallel treebank data performs optimally. Finally, we discuss the potential of parallel treebanks in other paradigms of MT