111,304 research outputs found

    GREAT: open source software for statistical machine translation

    Full text link
    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. Mach Transl 15(1-2): 75–103Andrés-Ferrer J, Juan-Císcar A, Casacuberta F (2008) Statistical estimation of rational transducers applied to machine translation. Appl Artif Intell 22(1–2): 4–22Bangalore S, Riccardi G (2002) Stochastic finite-state models for spoken language machine translation. Mach Transl 17(3): 165–184Berstel J (1979) Transductions and context-free languages. B.G. Teubner, Stuttgart, GermanyCasacuberta F, Vidal E (2004) Machine translation with inferred stochastic finite-state transducers. Comput Linguist 30(2): 205–225Casacuberta F, Vidal E (2007) Learning finite-state models for machine translation. Mach Learn 66(1): 69–91Foster G, Kuhn R, Johnson H (2006) Phrasetable smoothing for statistical machine translation. In: Proceedings of the 11th Conference on Empirical Methods in Natural Language Processing, Stroudsburg, PA, pp 53–61González J (2009) Aprendizaje de transductores estocásticos de estados finitos y su aplicación en traducción automática. 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. In: Proceedings of the 20th IEEE International Conference on Acoustic, Speech and Signal Processing, Detroit, MI, pp 181–184Knight K, Al-Onaizan Y (1998) Translation with finite-state devices. In: Proceedings of the 3rd Conference of the Association for Machine Translation in the Americas, Langhorne, PA, pp 421–437Koehn P (2004) Statistical significance tests for machine translation evaluation. In: Proceedings of the 9th Conference on Empirical Methods in Natural Language Processing, Barcelona, Spain, pp 388–395Koehn P (2005) Europarl: a parallel corpus for statistical machine translation. In: Proceedings of the 10th Machine Translation Summit, Phuket, Thailand, pp 79–86Koehn P (2010) Statistical machine translation. Cambridge University Press, Cambridge, UKKoehn P, Hoang H (2007) Factored translation models. In: Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Prague, Czech Republic, pp 868–876Koehn 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 45th Annual Meeting of the Association for Computational Linguistics, Prague, Czech Republic, pp 177–180Kumar S, Deng Y, Byrne W (2006) A weighted finite state transducer translation template model for statistical machine translation. Nat Lang Eng 12(1): 35–75Li Z, Callison-Burch C, Dyer C, Ganitkevitch J, Khudanpur S, Schwartz L, Thornton WNG, Weese J, Zaidan OF (2009) Joshua: an open source toolkit for parsing-based machine translation. In: Procee- dings of the ACL Workshop on Statistical Machine Translation, Morristown, NJ, pp 135–139Llorens D, Vilar JM, Casacuberta F (2002) Finite state language models smoothed using n-grams. Int J Pattern Recognit Artif Intell 16(3): 275–289Marcu D, Wong W (2002) A phrase-based, joint probability model for statistical machine translation. In: Proceedings of the 7th Conference on Empirical Methods in Natural Language Processing, Morristown, NJ, pp 133–139Mariño JB, Banchs RE, Crego JM, de Gispert A, Lambert P, Fonollosa JAR, Costa-jussà MR (2006) N-gram-based machine translation. Comput Linguist 32(4): 527–549Medvedev YT (1964) On the class of events representable in a finite automaton. In: Moore EF (eds) Sequential machines selected papers. Addison Wesley, Reading, MAMohri M, Pereira F, Riley M (2002) Weighted finite-state transducers in speech recognition. Comput Speech Lang 16(1): 69–88Och FJ, Ney H (2002) Discriminative training and maximum entropy models for statistical machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, Philadelphia, PA, pp 295–302Och FJ, Ney H (2003) A systematic comparison of various statistical alignment models. Comput Linguist 29(1): 19–51Ortiz D, García-Varea I, Casacuberta F (2005) Thot: a toolkit to train phrase-based statistical translation models. In: Proceedings of the 10th Machine Translation Summit, Phuket, Thailand, pp 141–148Papineni K, Roukos S, Ward T, Zhu WJ (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, Philadelphia, PA, pp 311–318Pérez A, Torres MI, Casacuberta F (2008) Joining linguistic and statistical methods for Spanish-to-Basque speech translation. Speech Commun 50: 1021–1033Picó D, Casacuberta F (2001) Some statistical-estimation methods for stochastic finite-state transducers. Mach Learn 44: 121–142Rosenfeld R (1996) A maximum entropy approach to adaptive statistical language modeling. Comput Speech Lang 10: 187–228Simard M, Plamondon P (1998) Bilingual sentence alignment: balancing robustness and accuracy. Mach Transl 13(1): 59–80Singh AK, Husain S (2007) Exploring translation similarities for building a better sentence aligner. In: Proceedings of the 3rd Indian International Conference on Artificial Intelligence, Pune, India, pp 1852–1863Steinbiss V, Tran BH, Ney H (1994) Improvements in beam search. In: Proceedings of the 3rd International Conference on Spoken Language Processing, Yokohama, Japan, pp 2143–2146Torres MI, Varona A (2001) k-TSS language models in speech recognition systems. Comput Speech Lang 15(2): 127–149Vidal E (1997) Finite-state speech-to-speech translation. In: Proceedings of the 22nd IEEE International Conference on Acoustic, Speech and Signal Processing, Munich, Germany, pp 111–114Vidal E, Thollard F, de la Higuera C, Casacuberta F, Carrasco RC (2005) Probabilistic finite-state machines–Part II. IEEE Trans Pattern Anal Mach Intell 27(7): 1025–1039Viterbi A (1967) Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Trans Inf Theory 13(2): 260–26

    Multilingual Neural Translation

    Get PDF
    Machine translation (MT) refers to the technology that can automatically translate contents in one language into other languages. Being an important research area in the field of natural language processing, machine translation has typically been considered one of most challenging yet exciting problems. Thanks to research progress in the data-driven statistical machine translation (SMT), MT is recently capable of providing adequate translation services in many language directions and it has been widely deployed in various practical applications and scenarios. Nevertheless, there exist several drawbacks in the SMT framework. The major drawbacks of SMT lie in its dependency in separate components, its simple modeling approach, and the ignorance of global context in the translation process. Those inherent drawbacks prevent the over-tuned SMT models to gain any noticeable improvements over its horizon. Furthermore, SMT is unable to formulate a multilingual approach in which more than two languages are involved. The typical workaround is to develop multiple pair-wise SMT systems and connect them in a complex bundle to perform multilingual translation. Those limitations have called out for innovative approaches to address them effectively. On the other hand, it is noticeable how research on artificial neural networks has progressed rapidly since the beginning of the last decade, thanks to the improvement in computation, i.e faster hardware. Among other machine learning approaches, neural networks are known to be able to capture complex dependencies and learn latent representations. Naturally, it is tempting to apply neural networks in machine translation. First attempts revolve around replacing SMT sub-components by the neural counterparts. Later attempts are more revolutionary by fundamentally changing the whole core of SMT with neural networks, which is now popularly known as neural machine translation (NMT). NMT is an end-to-end system which directly estimate the translation model between the source and target sentences. Furthermore, it is later discovered to capture the inherent hierarchical structure of natural language. This is the key property of NMT that enables a new training paradigm and a less complex approach for multilingual machine translation using neural models. This thesis plays an important role in the evolutional course of machine translation by contributing to the transition of using neural components in SMT to the completely end-to-end NMT and most importantly being the first of the pioneers in building a neural multilingual translation system. First, we proposed an advanced neural-based component: the neural network discriminative word lexicon, which provides a global coverage for the source sentence during the translation process. We aim to alleviate the problems of phrase-based SMT models that are caused by the way how phrase-pair likelihoods are estimated. Such models are unable to gather information from beyond the phrase boundaries. In contrast, our discriminative word lexicon facilitates both the local and global contexts of the source sentences and models the translation using deep neural architectures. Our model has improved the translation quality greatly when being applied in different translation tasks. Moreover, our proposed model has motivated the development of end-to-end NMT architectures later, where both of the source and target sentences are represented with deep neural networks. The second and also the most significant contribution of this thesis is the idea of extending an NMT system to a multilingual neural translation framework without modifying its architecture. Based on the ability of deep neural networks to modeling complex relationships and structures, we utilize NMT to learn and share the cross-lingual information to benefit all translation directions. In order to achieve that purpose, we present two steps: first in incorporating language information into training corpora so that the NMT learns a common semantic space across languages and then force the NMT to translate into the desired target languages. The compelling aspect of the approach compared to other multilingual methods, however, lies in the fact that our multilingual extension is conducted in the preprocessing phase, thus, no change needs to be done inside the NMT architecture. Our proposed method, a universal approach for multilingual MT, enables a seamless coupling with any NMT architecture, thus makes the multilingual expansion to the NMT systems effortlessly. Our experiments and the studies from others have successfully employed our approach with numerous different NMT architectures and show the universality of the approach. Our multilingual neural machine translation accommodates cross-lingual information in a learned common semantic space to improve altogether every translation direction. It is then effectively applied and evaluated in various scenarios. We develop a multilingual translation system that relies on both source and target data to boost up the quality of a single translation direction. Another system could be deployed as a multilingual translation system that only requires being trained once using a multilingual corpus but is able to translate between many languages simultaneously and the delivered quality is more favorable than many translation systems trained separately. Such a system able to learn from large corpora of well-resourced languages, such as English → German or English → French, has proved to enhance other translation direction of low-resourced language pairs like English → Lithuania or German → Romanian. Even more, we show that kind of approach can be applied to the extreme case of zero-resourced translation where no parallel data is available for training without the need of pivot techniques. The research topics of this thesis are not limited to broadening application scopes of our multilingual approach but we also focus on improving its efficiency in practice. Our multilingual models have been further improved to adequately address the multilingual systems whose number of languages is large. The proposed strategies demonstrate that they are effective at achieving better performance in multi-way translation scenarios with greatly reduced training time. Beyond academic evaluations, we could deploy the multilingual ideas in the lecture-themed spontaneous speech translation service (Lecture Translator) at KIT. Interestingly, a derivative product of our systems, the multilingual word embedding corpus available in a dozen of languages, can serve as a useful resource for cross-lingual applications such as cross-lingual document classification, information retrieval, textual entailment or question answering. Detailed analysis shows excellent performance with regard to semantic similarity metrics when using the embeddings on standard cross-lingual classification tasks

    A Survey of Word Reordering in Statistical Machine Translation: Computational Models and Language Phenomena

    Get PDF
    Word reordering is one of the most difficult aspects of statistical machine translation (SMT), and an important factor of its quality and efficiency. Despite the vast amount of research published to date, the interest of the community in this problem has not decreased, and no single method appears to be strongly dominant across language pairs. Instead, the choice of the optimal approach for a new translation task still seems to be mostly driven by empirical trials. To orientate the reader in this vast and complex research area, we present a comprehensive survey of word reordering viewed as a statistical modeling challenge and as a natural language phenomenon. The survey describes in detail how word reordering is modeled within different string-based and tree-based SMT frameworks and as a stand-alone task, including systematic overviews of the literature in advanced reordering modeling. We then question why some approaches are more successful than others in different language pairs. We argue that, besides measuring the amount of reordering, it is important to understand which kinds of reordering occur in a given language pair. To this end, we conduct a qualitative analysis of word reordering phenomena in a diverse sample of language pairs, based on a large collection of linguistic knowledge. Empirical results in the SMT literature are shown to support the hypothesis that a few linguistic facts can be very useful to anticipate the reordering characteristics of a language pair and to select the SMT framework that best suits them.Comment: 44 pages, to appear in Computational Linguistic

    Dependency relations as source context in phrase-based SMT

    Get PDF
    The Phrase-Based Statistical Machine Translation (PB-SMT) model has recently begun to include source context modeling, under the assumption that the proper lexical choice of an ambiguous word can be determined from the context in which it appears. Various types of lexical and syntactic features such as words, parts-of-speech, and supertags have been explored as effective source context in SMT. In this paper, we show that position-independent syntactic dependency relations of the head of a source phrase can be modeled as useful source context to improve target phrase selection and thereby improve overall performance of PB-SMT. On a Dutch—English translation task, by combining dependency relations and syntactic contextual features (part-of-speech), we achieved a 1.0 BLEU (Papineni et al., 2002) point improvement (3.1% relative) over the baseline

    Statistical Machine Translation Features with Multitask Tensor Networks

    Full text link
    We present a three-pronged approach to improving Statistical Machine Translation (SMT), building on recent success in the application of neural networks to SMT. First, we propose new features based on neural networks to model various non-local translation phenomena. Second, we augment the architecture of the neural network with tensor layers that capture important higher-order interaction among the network units. Third, we apply multitask learning to estimate the neural network parameters jointly. Each of our proposed methods results in significant improvements that are complementary. The overall improvement is +2.7 and +1.8 BLEU points for Arabic-English and Chinese-English translation over a state-of-the-art system that already includes neural network features.Comment: 11 pages (9 content + 2 references), 2 figures, accepted to ACL 2015 as a long pape
    corecore