W. Aziz, S. C. De-sousa, and L. Specia, PET: a Tool for Post-editing and Assessing Machine Translation, pp.3982-3987, 2012.

D. Bahdanau, K. Cho, and Y. Bengio, Neural Machine Translation by Jointly Learning to Align and Translate, ICLR 2015, pp.3104-3112, 2015.

L. Bentivogli, A. Bisazza, and M. Cettolp, Neural versus Phrase-Based Machine Translation Quality: A Case Study, 2016.
DOI : 10.18653/v1/d16-1025
URL : https://doi.org/10.18653/v1/d16-1025

A. Bérard, O. Pietquin, L. Besacier, and C. Servan, Listen and Translate: A Proof of Concept for End-to-End Speech-to-Text Translation. NIPS Workshop on end-to-end learning for speech and audio processing, 2016.

O. Bojar, R. Chatterjee, C. Federmann, Y. Graham, B. Haddow et al., Proceedings of the First Conference on Machine Translation, pp.131-198, 2016.

F. Brunet-manquat and E. Esperança-rodier, ACCOLÉ : Annotation Collaborative d'erreurs de traduction pour COrpus aLignÉs, 2018.

S. Castilho, J. Moorkens, F. Gaspari, R. Sennrich, V. Sosoni et al., A Comparative Quality Evaluation of PBSMT and NMT using Professional Translators, Proceedings of Machine Translation Summit XVI, 2017.

J. K. Chorowski, D. Bahdanau, D. Serdyuk, K. Cho, and Y. Bengio, AttentionBased Models for Speech Recognition, Advances in Neural Information Processing Systems (NIPS 2015), pp.577-585, 2015.

E. Esperanca-rodier, C. Rossi, A. Berard, and L. Besacier, Evaluation of NMT and SMT Systems: A Study on Uses and Perceptions dans Proceedings of the 39th, 2017.

, Conference Translating and the Computer, pp.11-24, 2017.

P. Isabelle, C. Cherry, and G. Foster, A Challenge Set Approach to Evaluating Machine Translation dans, Natural Language Processing (EMNLP), pp.2486-2496, 2017.
DOI : 10.18653/v1/d17-1263
URL : https://doi.org/10.18653/v1/d17-1263

D. Kingma and J. Ba, Adam: A Method for Stochastic Optimization dans International Conference on Learning Representations, 2015.

P. Koehn, H. Hoang, A. Birch, C. Callison-burch, M. Federico et al., Moses: Open source toolkit for statistical machine translation, Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions, pp.177-180, 2007.

K. Papineni, S. Roukos, T. Ward, and W. Zhu, BLEU: A Method for Automatic Evaluation of Machine Translation, Proceedings of the 40th Annual Meeting on Associa-tion for Computational Linguistics, pp.311-318, 2002.

I. Sutskever, O. Vinyals, L. , and Q. V. , Sequence to Sequence Learning with Neural Networks, Advances in Neural Information Processing Systems (NIPS 2014), pp.3104-3112, 2014.

M. Snover, B. Dorr, R. Schwartz, L. Micciulla, and M. Makhoul, A study of translation edit rate with targeted human annotation, Proc. of AMTA, 2006.

C. Servan, A. Bérard, Z. Elloumi, H. Blanchon, and L. Besacier, Word2Vec vs DBnary: Augmenting METEOR using Vector Representations or Lexical Resources? COLING, 26th International Conference on Computational Linguistics (COLING 2016, p.2016, 2016.

A. Toral and V. M. Sanchez-cartagena, A Multifaceted Evaluation of Neural versus Phrase-Based Machine Translation for 9 Language Directions, Proceedings of the 15th Conference of the European Chapter, vol.1, pp.1063-1073, 2017.

D. Vilar, J. Xu, and L. F. D'haro, Error analysis of statistical machine translation output, Proceedings of LREC, pp.697-702, 2006.

Y. Wu, M. Schuster, Z. Chen, Q. V. Le, M. Norouzi et al., Google's Neural Machine Translation System, 2016.

W. Zaremba, I. Sutskever, and O. Vinyals, Recurrent neural network regularization, 2014.