Abstract
Translation quality estimation (QE) is a task of estimating the quality of translation output from an unknown machine translation (MT) system without reference at various granularity (sentence/word/phrase) levels, and it has been attracting much attention due to the potential to reduce post-editing human effort. However, QE suffers heavily from the fact that the quality annotation data remain expensive and small. In this paper, we focus on the limited QE data problem and seek to find how to utilize the high level latent features learned by the pre-trained language models for improving QE. Specifically, we explore three strategies to integrate the pre-trained language representations into QE models: (1) a mixed integration model, where the pre-trained language features are mixed with other features for QE; (2) a direct integration model, which regards the pre-trained language model as the only feature extracting component of the entire QE model; and (3) a constrained integration model, where a constraint mechanism is added to optimize the quality prediction based on the direct integration model. Experiments and analysis presented in this paper demonstrate the effectiveness of our approaches on QE task.
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References
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. In: Proceedings of ICLR 2015 (2015)
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N.: Kaiser: attention is all you need. arXiv preprint arXiv:1601.03317 (2017)
Felice, M., Specia, L.: Linguistic features for quality estimation. In: Proceedings of the 7th Workshop on Statistical Machine Translation. Association for Computational Linguistics, pp. 96–103 (2012)
Specia, L., Shah, K., de Souza, J.G.C., Cohn, T.: QuEst - a translation quality estimation framework. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 79–84. Association for Computational Linguistics (2013)
Kozlova, A., Shmatova, M., Frolov, A.: YSDA participation in the WMT’16 quality estimation shared task. In: Proceedings of the 1st Conference on Machine Translation, pp. 793–799. Association for Computational Linguistics (2016)
Kreutzer, J., Schamoni, S., Riezler, S.: QUality estimation from ScraTCH (QUETCH): deep learning for word-level translation quality estimation. In: Proceedings of the 10th Workshop on Statistical Machine Translation, pp. 316–322. Association for Computational Linguistics (2015)
Martins, A.F.T., Astudillo, R., Hokamp, C., Kepler, F.: Unbabel’s participation in the WMT16 wordlevel translation quality estimation shared task. In: Proceedings of the 1st Conference on Machine Translation, pp. 806–811. Association for Computational Linguistics (2016)
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. Inform. Process. (TALLIP) 17(1), 3 (2017)
Fan, K., Wang, J., Li, B., et al.: “Bilingual Expert” can find translation errors. In: National Conference on Artificial Intelligence (2019)
Peters, M.E., Neumann, M., Iyyer, M., et al.: Deep contextualized word representations. arXiv preprint arXiv:1802.05365 (2018)
Radford, A., Narasimhan, K., Salimans, T., Sutskever, I.: Improving language understanding with unsupervised learning. Technical report, OpenAI (2018)
Devlin, J., Chang, M.W., Lee, K., et al.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Wu, Y., et al.: Google’s neural machine translation system: bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144 (2016)
Gehring, J., Auli, M., Grangier, D., Yarats, D., Dauphin, Y.N.: Convolutional sequence to sequence learning. arXiv preprint arXiv:1601.03317 (2017)
Luong, M.-T., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation. In: Proceedings of EMNLP 2015, pp. 1412–1421 (2015)
Dai, A.M., Le, Q.V.: Semi-supervised sequence learning. In: Advances in Neural Information Processing Systems, pp. 3079–3087 (2015)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: NIPS (2013)
Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: EMNLP (2014)
Wu, Y., Schuster, M., Chen, Z., et al.: Google’s neural machine translation system: bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144 (2016)
Dyer, C., Chahuneau, V., Smith, N.A.: A simple, fast, and effective reparameterization of IBM model 2. In: Proceedings of NAACL 2013 (2013)
Acknowledgements
This work is supported by the National Nature Science Foundation of China (Nos. 61370130, 61473294 and 61876198), the Fundamental Research Funds for the Central Universities (2015JBM033), the International Science and Technology Cooperation Program of China under grant No. K11F100010, the Fundamental Research Funds for the Central Universities (No. 2018YJS043), Major Projects of Fundamental Research on Philosophy and Social Sciences of Henan Education Department (2016-JCZD-022), and Toshiba (China) Co., Ltd.
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Miao, G., Di, H., Xu, J., Yang, Z., Chen, Y., Ouchi, K. (2019). Improved Quality Estimation of Machine Translation with Pre-trained Language Representation. In: Tang, J., Kan, MY., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2019. Lecture Notes in Computer Science(), vol 11838. Springer, Cham. https://doi.org/10.1007/978-3-030-32233-5_32
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