Improved Pseudo Data for Machine Translation Quality Estimation with Constrained Beam Search
Published in EMNLP2023, 2023
Recommended citation: Geng, X., Zhang, Y., Lai, Z., She, S., Zou, W., Tao, S., Yang, H., Chen, J., & Huang, S. (2023). Improved Pseudo Data for Machine Translation Quality Estimation with Constrained Beam Search. Conference on Empirical Methods in Natural Language Processing. https://aclanthology.org/2023.emnlp-main.764.pdf
The study introduces CBSQE, a method for generating more accurate pseudo data for machine translation quality estimation by using constrained beam search to differentiate between likely correct and incorrect translation segments, improving performance in both supervised and unsupervised settings.
Recommended citation: Geng, X., Zhang, Y., Lai, Z., She, S., Zou, W., Tao, S., Yang, H., Chen, J., & Huang, S. (2023). Improved Pseudo Data for Machine Translation Quality Estimation with Constrained Beam Search. Conference on Empirical Methods in Natural Language Processing.