Abstract
In this paper, we propose a novel approach learning bilingual representations to predict quality estimation of machine translation. We use two bi-directional Long Short-Term Memory (LSTM) based architectures map the source sentence and target sentence to two context vector of a fixed dimensionality, then we compute the weighted cosine distance of the two vectors to estimate the translation quality of the target sentence. Our experimental results show that our model improve the performance over a baseline system with 17 features in the English-to-Spanish sentence-level quality estimation task of WMT15.
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References
Blatz, J., Fitzgerald, E., Foster, G., Gandrabur, S., Goutte, C., Kulesza, A., Sanchis, A., Ueffing, N.: Confidence estimation for machine translation. In: Proceedings of the 20th International Conference on Computational Linguistics, p. 315. Association for Computational Linguistics (2004)
Bojar, O., Buck, C., Callison-Burch, C., Federmann, C., Haddow, B., Koehn, P., Monz, C., Post, M., Soricut, R., Specia, L.: Findings of the 2013 workshop on statistical machine translation. In: Proceedings of the Eighth Workshop on Statistical Machine Translation, pp. 1–44. Association for Computational Linguistics, Sofia, Bulgaria (2013)
Bojar, O., Chatterjee, R., Federmann, C., Haddow, B., Huck, M., Hokamp, C., Koehn, P., Logacheva, V., Monz, C., Negri, M., Post, M., Scarton, C., Specia, L., Turchi, M.: Findings of the 2015 workshop on statistical machine translation. In: Proceedings of the Tenth Workshop on Statistical Machine Translation, pp. 1–46. Association for Computational Linguistics, Lisbon, Portugal (2015)
Bojar, O., Buck, C., Federmann, C., Haddow, B., Koehn, P., Leveling, J., Monz, C., Pecina, P., Post, M., Saint-Amand, H., Soricut, R., Specia, L., Tamchyna, A.: Findings of the 2014 workshop on statistical machine translation. In: Proceedings of the Ninth Workshop on Statistical Machine Translation, pp. 12–58. Association for Computational Linguistics, Baltimore, Maryland, USA (2014)
Callison-Burch, C., Koehn, P., Monz, C., Post, M., Soricut, R., Specia, L.: Findings of the 2012 workshop on statistical machine translation. In: Proceedings of the Seventh Workshop on Statistical Machine Translation, pp. 10–51. Association for Computational Linguistics, Montréal, Canada (2012)
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. In: ICLR 2015, pp. 1–15 (2014). http://arxiv.org/abs/1409.0473v3
Gers, F.A., Schmidhuber, J., Cummins, F.: Learning to forget: continual prediction with LSTM. Neural Comput. 12(10), 2451–2471 (2000)
Gers, F.A., Schraudolph, N.N., Schmidhuber, J.: Learning precise timing with LSTM recurrent networks. J. Mach. Learn. Res. 3, 115–143 (2002)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Koehn, P.: Europarl: a parallel corpus for statistical machine translation. In: MT summit, vol. 5, pp. 79–86 (2005)
Langlois, D.: LORIA system for the WMT15 quality estimation shared task. In: Proceedings of the Tenth Workshop on Statistical Machine Translation, pp. 323–329. Association for Computational Linguistics, Lisbon, Portugal, September 2015
Luong, M.T., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation (2015)
Shah, K., Logacheva, V., Paetzold, G., Blain, F., Beck, D., Bougares, F., Specia, L.: SHEF-NN: translation quality estimation with neural networks. In: Proceedings of the Tenth Workshop on Statistical Machine Translation, pp. 342–347, no. September. Association for Computational Linguistics, September 2015
Snover, M., Dorr, B., Schwartz, R., Micciulla, L., Makhoul, J.: A study of translation edit rate with targeted human annotation. In: Proceedings of Association for Machine Translation in the Americas, pp. 223–231 (2006)
Specia, L., Cancedda, N., Dymetman, M., Turchi, M., Cristianini, N.: Estimating the sentence-level quality of machine translation systems. In: EAMT-2009: 13th Annual Conference of the European Association for Machine Translation, pp. 28–35 (2009)
Specia, L., Paetzold, G., Scarton, C.: Multi-level translation quality prediction with quest++. In: Proceedings of ACL-IJCNLP 2015 System Demonstrations, pp. 115–120. Association for Computational Linguistics and The Asian Federation of Natural Language Processing, Beijing, China, July 2015. http://www.aclweb.org/anthology/P15-4020
Specia, L., Shah, K., de Souza, J.G.C., Cohn, T., Kessler, F.B.: Quest a translation quality estimation framework. In: Proceedings of the 51st ACL: System Demonstrations, pp. 79–84 (2013)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems (NIPS), pp. 3104–3112 (2014). http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural
Zeiler, M.D.: Adadelta: an adaptive learning rate method. arXiv preprint arXiv:1212.5701 (2012)
Acknowledgements
This paper is supported by the project of Natural Science Foundation of China (Grant No. 61272384 & 61402134 & 61370170).
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Zhu, J., Yang, M., Li, S., Zhao, T. (2016). Learning Bilingual Sentence Representations for Quality Estimation of Machine Translation. In: Yang, M., Liu, S. (eds) Machine Translation. CWMT 2016. Communications in Computer and Information Science, vol 668. Springer, Singapore. https://doi.org/10.1007/978-981-10-3635-4_4
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