Abstract
Exposure bias and poor translation diversity are two common problems in neural machine translation (NMT), which are caused by the general of the teacher forcing strategy for training in the NMT models. Moreover, the NMT models usually require the large-scale and high-quality parallel corpus. However, Korean is a low resource language, and there is no large-scale parallel corpus between Chinese and Korean, which is a challenging for the researchers. Therefore, we propose a method which is to incorporate translation quality estimation into the translation process and adopt reinforcement learning. The evaluation mechanism is used to guide the training of the model, so that the prediction cannot converge completely to the ground truth word. When the model predicts a sequence different from the ground truth word, the evaluation mechanism can give an appropriate evaluation and reward to the model. In addition, we alleviated the lack of Korean corpus resources by adding training data. In our experiment, we introduce a monolingual corpus of a certain scale to construct pseudo-parallel data. At the same time, we also preprocessed the Korean corpus with different granularities to overcome the data sparsity. Experimental results show that our work is superior to the baselines in Chinese-Korean and Korean-Chinese translation tasks, which fully certificates the effectiveness of our method.
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
Bahdanau, D., Cho, K.H., Bengio, Y.: Neural machine translation by jointly learning to align and translate. In: 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, pp. 1–15 (2015)
Fan, K., Wang, J, Li, B.: Bilingual expert1 can find translation errors. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 6367–6374 (2019)
Junczys-Dowmunt, M., Dwojak, T., Hoang, H.: Is neural machine translation ready for deployment? In: A Case Study on 30 Translation Directions (2016)
Keneshloo, Y., Shi, T., Ramakrishnan, N.: Deep reinforcement learning for sequence-to-sequence models, pp. 2469–2489 (2020)
Kim, H., Lee, J.-H., Na, S.H.: Predictor-estimator using multilevel task learning with stack propagation for neural quality estimation. In: Proceedings of the 2nd Conference on Machine Translation, pp. 562–568 (2017)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: 3rd International Conference on Learning Representations (ICLR), pp. 1–15 (2015)
Lavie, A., Agarwal, A.: Meteor: an automatic metric for MT evaluation with high levels of correlation with human judgments. In: Proceedings of the Second Workshop on Statistical Machine Translation, pp. 228–231 (2007)
Papineni, K., Roukos, S., Zhu, W.-T.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, 2002, pp. 311–318 (2002)
Ranzato, M., Chopra, S., Auli, M.: Sequence level training with recurrent neural networks. In: 4th International Conference on Learning Representations (ICLR 2016), pp. 1–16 (2016)
Shiqi, S., Yong, C., He, Z.: Minimum risk training for neural machine translation. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, pp. 1683–1692. Association for Computational Linguistics. (2016)
Silver, D., Lever, G., Hees, N.: Deterministic policy gradient algorithms. In: 31st International Conference on Machine Learning (ICML), pp. 605–619 (2014)
Snover, M., Dorr, B., Schwartz, R.: A study of translation edit rate with targeted human annotation. In: Proceedings of the 7th Conference of the Association for Machine Translation of the Americas: Visions for the Future of Machine Translation (AMTA), pp. 223–231 (2006)
Specia, L., Shah, K., de Souza, J.: Quest++a translation quality estimation framework. In: Proceedings of the 51st ACL: System Demonstrations, pp. 79–84 (2013)
Mingjie, T., Yahui, Z., Cui, R.: Identifying word translations in scientific literature based on labeled bilingual topic model and co-occurrence features. In: Proceedings of Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data, pp. 76–87 (2018)
Vaswani, A., Shazeer, N., Parmar, N.: Attention is all you need. In: The proceedings of Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems. pp. 5998–6008 (2017)
Weaver, L., Tao, N.: The optimal reward baseline for gradient-based reinforcement learning. In: Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence, pp. 538–545 (1999)
Williams, R.J., Zipser, D.: A learning algorithm for continually running fully recurrent neural networks. Neural Computation, 1, 270–280 (1989)
Wu, L., Tian, F., Qin, T.: A study of reinforcement learning for neural machine translation. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 3612–3621 (2018)
Zhen, Y., Wei, C., Wang, F.: Improving neural machine translate on with conditional sequence generative adversarial nets p. arXiv preprint arXiv:1703.04887 (2017)
Yongshou, J.: Current situation and future research direction of Chinese-Korean translation theory. In: Korean Language in China, pp. 66–73 (2020)
Zhang, W., Feng, Y., Meng, F.: Bridging the gap between training and inference for neural machine translation. In: 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, ACL. arXiv preprint arXiv:1906.02448 (2019)
Acknowledgements
This research work has been funded by the National Language Commission Scientific Research Project (YB135-76), the Yanbian University Foreign Language and Literature First-Class Subject Construction Project (18YLPY13).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Li, F., Zhao, Y., Yang, F., Cui, R. (2021). Incorporating Translation Quality Estimation into Chinese-Korean Neural Machine Translation. In: Li, S., et al. Chinese Computational Linguistics. CCL 2021. Lecture Notes in Computer Science(), vol 12869. Springer, Cham. https://doi.org/10.1007/978-3-030-84186-7_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-84186-7_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-84185-0
Online ISBN: 978-3-030-84186-7
eBook Packages: Computer ScienceComputer Science (R0)