Abstract
Most state-of-the-art QE systems built upon neural networks have achieved promising performances on benchmark datasets. However, the performance of these methods can be easily influenced by the inherent features of the model input, such as the length of input sequence or the number of unseen tokens. In this paper, we introduce a causal inference based method to eliminate the negative impact caused by the characters of the input for a QE system. Specifically, we propose an iterative denoising framework for multiple confounding features. The confounder elimination operation at each iteration step is implemented by a Half-Sibling Regression based method. We conduct our experiments on the official datasets and submissions from WMT 2020 Quality Estimation Shared Task of Sentence-Level Direct Assessment. Experimental results show that the denoised QE results gain better Pearson’s correlation scores with human assessments compared to the original submissions.
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References
Barrault, L., et al.: Findings of the 2020 conference on machine translation (WMT20). In: Proceedings of the Fifth Conference on Machine Translation, pp. 1–55. Association for Computational Linguistics, Online (November 2020)
Koehn, P., Knowles, R.: Six challenges for neural machine translation. In: Proceedings of the First Workshop on Neural Machine Translation, pp. 28–39. Association for Computational Linguistics, Vancouver (August 2017)
Ott, M., Auli, M., Grangier, D., Ranzato, M.: Analyzing uncertainty in neural machine translation. In: Dy, J.G., Krause, A. (eds.) Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm, Sweden, July 10–15, 2018. Proceedings of Machine Learning Research, vol. 80, pp. 3953–3962. PMLR (2018)
Schölkopf, B., et al.: Modeling confounding by half-sibling regression. Proc. Natl. Acad. Sci. USA 113(27), 7391–7398 (2016)
Specia, L., Blain, F., Fomicheva, M., Fonseca, E., Chaudhary, V., Guzmán, F., Martins, A.F.T.: In: Findings of the WMT 2020 shared task on quality estimation, pp. 743–764. Association for Computational Linguistics, Online (November 2020)
Specia, L., Turchi, M., Cancedda, N., Cristianini, N., Dymetman, M.: Estimating the sentence-level quality of machine translation systems. In: Proceedings of the 13th Annual conference of the European Association for Machine Translation. European Association for Machine Translation, Barcelona, Spain (May 14–15 2009)
Acknowledgments
This work is supported by the National Key Research and Development Program of China (Grant No. 2017YFB1002103) and the National Natural Science Foundation of China (No. 61732005).
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Shi, X., Huang, H., Jian, P., Tang, YK. (2021). Removing Input Confounder for Translation Quality Estimation via a Causal Motivated Method. In: U, L.H., Spaniol, M., Sakurai, Y., Chen, J. (eds) Web and Big Data. APWeb-WAIM 2021. Lecture Notes in Computer Science(), vol 12858. Springer, Cham. https://doi.org/10.1007/978-3-030-85896-4_28
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DOI: https://doi.org/10.1007/978-3-030-85896-4_28
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