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Semi-supervised learning for Quality Estimation of Machine Translation

Tarun Bhatia; Martin Krämer; Eduardo Vellasquez; Eleftherios Avramidis
In: Proceedings of the 19th Machine Translation Summit. Machine Translation Summit (MT Summit-2023), September 4-8, Macau SAR, China, Pages 72-83, Vol. 1: Research Track, Asia-Pacific Association for Machine Translation (AAMT), 9/2023.


We investigate whether using semi-supervised learning (SSL) methods can be beneficial for the task of word-level Quality Estimation of Machine Translation in low resource conditions. We show that the Mean Teacher network can provide equal or significantly better MCC scores (up to +12%) than supervised methods when a limited amount of labeled data is available. Additionally, following previous work on SSL, we investigate Pseudo-Labeling in combination with SSL, which nevertheless does not provide consistent improvements.