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. 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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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Automated Testing of Speech-to-Speech Machine Translation in Telecom Networks

Globalisoituvassa maailmassa kyky kommunikoida kielimuurien yli käy yhä tärkeämmäksi. Kielten opiskelu on työlästä ja siksi halutaan kehittää automaattisia konekäännösjärjestelmiä. Ericsson on kehittänyt prototyypin nimeltä Real-Time Interpretation System (RTIS), joka toimii mobiiliverkossa ja kääntää matkailuun liittyviä fraaseja puhemuodossa kahden kielen välillä. Nykyisten konekäännösjärjestelmien suorituskyky on suhteellisen huono ja siksi testauksella on suuri merkitys järjestelmien suunnittelussa. Testauksen tarkoituksena on varmistaa, että järjestelmä säilyttää käännösekvivalenssin sekä puhekäännösjärjestelmän tapauksessa myös riittävän puheenlaadun. Luotettavimmin testaus voidaan suorittaa ihmisten antamiin arviointeihin perustuen, mutta tällaisen testauksen kustannukset ovat suuria ja tulokset subjektiivisia. Tässä työssä suunniteltiin ja analysoitiin automatisoitu testiympäristö Real-Time Interpretation System -käännösprototyypille. Tavoitteina oli tutkia, voidaanko testaus suorittaa automatisoidusti ja pystytäänkö todellinen, käyttäjän havaitsema käännösten laatu mittaamaan automatisoidun testauksen keinoin. Tulokset osoittavat että mobiiliverkoissa puheenlaadun testaukseen käytetyt menetelmät eivät ole optimaalisesti sovellettavissa konekäännösten testaukseen. Nykytuntemuksen mukaan ihmisten suorittama arviointi on ainoa luotettava tapa mitata käännösekvivalenssia ja puheen ymmärrettävyyttä. Konekäännösten testauksen automatisointi vaatii lisää tutkimusta, jota ennen subjektiivinen arviointi tulisi säilyttää ensisijaisena testausmenetelmänä RTIS-testauksessa.In the globalizing world, the ability to communicate over language barriers is increasingly important. Learning languages is laborious, which is why there is a strong desire to develop automatic machine translation applications. Ericsson has developed a speech-to-speech translation prototype called the Real-Time Interpretation System (RTIS). The service runs in a mobile network and translates travel phrases between two languages in speech format. The state-of-the-art machine translation systems suffer from a relatively poor performance and therefore evaluation plays a big role in machine translation development. The purpose of evaluation is to ensure the system preserves the translational equivalence, and in case of a speech-to-speech system, the speech quality. The evaluation is most reliably done by human judges. However, human-conducted evaluation is costly and subjective. In this thesis, a test environment for Ericsson Real-Time Interpretation System prototype is designed and analyzed. The goals are to investigate if the RTIS verification can be conducted automatically, and if the test environment can truthfully measure the end-to-end performance of the system. The results conclude that methods used in end-to-end speech quality verification in mobile networks can not be optimally adapted for machine translation evaluation. With current knowledge, human-conducted evaluation is the only method that can truthfully measure translational equivalence and the speech intelligibility. Automating machine translation evaluation needs further research, until which human-conducted evaluation should remain the preferred method in RTIS verification

Incorporating Language Models into Non-autoregressive Neural Machine Translation

V této práci navrhujeme způsob pro zlepšení plynulosti výstupu neautoregresivního modelu pro neuronový strojový překlad. Využíváme k tomu rozšířený model pro počítání skóre během paprskového prohledávání. Skóre vypočítáváme jako lineární kombinaci dílčích skóre pocházejících z n-gramového jazykového modelu a dalších pomocných příznaků. Váhy pro lineární kombinaci určujeme pomocí strukturovaného perceptronu. Pro vyhodnocení rychlosti a kvality překladu trénujeme modely pro tři dvojice jazyků. Výsledky ukazují, že modely s navrženým vylepšením jsou stále dostatečně efektivní z hlediska rychlosti a zároveň dosahují výsledků srovnatelných s autoregresivními modely.In order to improve the fluency of a non-autoregressive model for neural machine translation, we propose an extension for the scoring model used during the beam search decoding. We compute the score as a linear combination of feature values, including the score from an n-gram language model and other auxiliary features. We determine the weights of the features using the structured perceptron algorithm. We train the models for three language pairs and evaluate their decoding speed and translation quality. The results show that our proposed models are still efficient in terms of decoding speed while achieving a competitive score relative to autoregressive models