Abstract
This paper presents our submissions to CCMT 2020 Quality Estimation (QE) sentence-level task for both Chinese-to-English (ZH-EN) and English-to-Chinese (EN-ZH). We propose new methods based on the predictor-estimator architecture. For the predictor, we propose XLM-predictor and Transformer-predictor. XLM-predictor novelly produces two kinds of contextual token representation, i.e., mask-XLM and non-mask-XLM. For the estimator, both RNN-estimator and Transformer-estimator are conducted and two novel strategies, i.e. top-K strategy and multi-head attention strategy, are proposed to enhance the sentence feature representation. We also propose new effective ensemble technique for sentence-level predictions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Fan, K., Wang, J., Li, B., Zhou, F., Chen, B., Si, L.: “Bilingual Expert” Can Find Translation Errors. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 6367–6374 (2019)
Fonseca, E., Yankovskaya, L., Martins, A.F., Fishel, M., Federmann, C.: Findings of the WMT 2019 shared tasks on quality estimation. In: Proceedings of the Fourth Conference on Machine Translation, vol. 3, pp. 1–10. ACL, Florence (2019)
Kepler, F., et al.: Unbabel’ s participation in the WMT19 translation quality estimation shared task. In: Proceedings of the Fourth Conference on Machine Translation, pp. 78–84. ACL, Florence (2019)
Kepler, F., Trénous, J., Treviso, M., Vera, M., Martins, A.F.: OpenKiwi: an open source framework for quality estimation. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 117–122. ACL, Florence (2019)
Kim, H., Jung, H.Y., Kwon, H., Lee, J.H., Na, S.H.: Predictor-estimator: neural quality estimation based on target word prediction for machine translation. ACM Trans. Asian Low-Resource Lang. Inf. Process. 17(1), 1–22 (2017)
Lample, G., Conneau, A.: Cross-lingual Language Model Pretraining. In: Advances in Neural Information Processing Systems 32, pp. 7059–7069. NeurIPS, Vancouver (2019)
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 of the Association for Computational Linguistics, pp. 311–318. ACL, Philadelphia (2002)
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. AMTA, Cambridge (2006)
Specia, L., Paetzold, G., Scarton, C.: Multi-level translation quality prediction with QuEst++. In: Proceedings of ACL-IJCNLP 2015 System Demonstrations, pp. 115–120. ACL-IJCNLP, Beijing (2015)
Wang, Z., et al.: NiuTrans submission for CCMT19 quality estimation task. In: Huang, S., Knight, K. (eds.) CCMT 2019. CCIS, vol. 1104, pp. 82–92. Springer, Singapore (2019). https://doi.org/10.1007/978-981-15-1721-1_9
Yang, M., et al.: CCMT 2019 machine translation evaluation report. In: Huang, S., Knight, K. (eds.) CCMT 2019. CCIS, vol. 1104, pp. 105–128. Springer, Singapore (2019). https://doi.org/10.1007/978-981-15-1721-1_11
Kepler F, Trénous J, Treviso M, et al.: Unbabel’s Participation in the WMT19 Translation Quality Estimation Shared Task. arXiv preprint arXiv:1907.10352 (2019)
Wolpert, D.H.: Stacked generalization. Neural Netw. 5(2), 241–259 (1992)
Breiman, L.: Stacked regressions. Mach. Learn. 24(1), 49–64 (1996)
Martins, A.F.T., Junczys-Dowmunt, M., Kepler, F.N., et al.: Pushing the limits of translation quality estimation. Trans. Assoc. Comput. Linguist. 5, 205–218 (2017)
Powell, M.J.D.: An efficient method for finding the minimum of a function of several variables without calculating derivatives. Comput. J. 7(2), 155–162 (1964)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wang, Z. et al. (2020). Tencent Submissions for the CCMT 2020 Quality Estimation Task. In: Li, J., Way, A. (eds) Machine Translation. CCMT 2020. Communications in Computer and Information Science, vol 1328. Springer, Singapore. https://doi.org/10.1007/978-981-33-6162-1_12
Download citation
DOI: https://doi.org/10.1007/978-981-33-6162-1_12
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-33-6161-4
Online ISBN: 978-981-33-6162-1
eBook Packages: Computer ScienceComputer Science (R0)