A Translation Evaluation Function based on Neural Network

• Autorzy: Douib A. , Langlois D. , Smaïli K.

Identyfikatory

DOI

10.4467/20838476SI.16.011.6192

• Języki publikacji: EN

Abstrakty

EN

In this paper, we study the feasibility of using a neural network to learn a fitness function for a machine translation system based on a genetic algorithm termed GAMaT. The neural network is learned on features extracted from pairs of source sentences and their translations. The fitness function is trained in order to estimate the BLEU of a translation as precisely as possible. The estimator has been trained on a corpus of more than 1.3 million data. The performance is very promising: the difference between the real BLEU and the one given by the estimator is equal to 0.12 in terms of Mean Absolute Error.

Słowa kluczowe

EN

statistical machine translation; genetic algorithm; quality estimation; neural network

• Wydawca: Wydawnictwo Uniwersytetu Jagiellońskiego
• Czasopismo: Schedae Informaticae
• Rocznik: 2016
• Tom: Vol. 25
• Strony: 139--151
• Opis fizyczny: Bibliogr. 14 poz., rys.

Twórcy

autor

Douib A.

• Université de Lorraine, Loria, Campus Scientifique, BP 239, 54506 Vandoeuvre-lés-Nancy, France

autor

Langlois D.

• Université de Lorraine, Loria, Campus Scientifique, BP 239, 54506 Vandoeuvre-lés-Nancy, France

autor

Smaïli K.

• Université de Lorraine, Loria, Campus Scientifique, BP 239, 54506 Vandoeuvre-lés-Nancy, France

Bibliografia

• [1] Koehn P., Hoang H., Birch A., Callison-Burch C., Federico M., Bertoldi N., Cowan B., Shen W., Moran C., Zens R., et al., Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th annual meeting of the ACL on interactive poster and demonstration sessions, Association for Computational Linguistics, 2007, pp. 177–180.
• [2] Douib A., Langlois D., Smaili K., Genetic-based decoder for statistical machine translation. December 2016, Nous n’avons pas encore la date officielle de publication.
• [3] Papineni K., Roukos S., Ward T., Zhu W.J., BLEU: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting on association for computational linguistics, Association for Computational Linguistics, 2002, pp. 311–318.
• [4] Bojar O., Chatterjee R., Federmann C., Haddow B., Hokamp C., Huck M., Logacheva V., Pecina P., eds. Proceedings of the Tenth Workshop on Statistical Machine Translation. Association for Computational Linguistics, September 2015, ,.
• [5] Neubig G.,Watanabe T., Optimization for statistical machine translation: A survey.Computational Linguistics, 2016.
• [6] Och F.J., Minimum error rate training in statistical machine translation. In: Proceedings of the 41st Annual Meeting on Association for Computational Linguistics-Volume 1, Association for Computational Linguistics, 2003, pp. 160–167.
• [7] Ondrej B. at. al, eds. Proceedings of the First Conference on Machine Translation. Association for Computational Linguistics, August 2016.
• [8] Langlois D., Loria system for the wmt15 quality estimation shared task. In: Proceedings of the Tenth Workshop on Statistical Machine Translation, Lisbon, Portugal, September 2015, pp. 323–329.
• [9] Kim H., Lee J.H., A recurrent neural networks approach for estimating the quality of machine translation output. In: Proceedings of NAACL-HLT, 2016, pp. 494–498.
• [10] Koehn P., Och F.J., Marcu D., Statistical phrase-based translation. In: Proceedingsof the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology-Volume 1, Association for Computational Linguistics, 2003, pp. 48–54.
• [11] Bergstra J., Bastien F., Breuleux O., Lamblin P., Pascanu R., Delalleau O.,Desjardins G., Warde-Farley D., Goodfellow I., Bergeron A., et al., Theano: Deep learning on gpus with python. In: NIPS 2011, BigLearning Workshop, Granada, Spain, Citeseer, 2011.
• [12] Bojar O., Buck C., Federmann C., Haddow B., Koehn P., Leveling J., Monz C., Pecina P., Post M., Saint-Amand H., et al., Findings of the 2014 workshop on statistical machine translation. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, Association for Computational Linguistics Baltimore, MD, USA, 2014, pp. 12–58.
• [13] Och F.J., Ney H., A systematic comparison of various statistical alignment models. Computational linguistics, 2003, 29(1), pp. 19–51.
• [14] Willmott C.J., Matsuura K., Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate research, 2005, 30(1), pp. 79–82.

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).