Martin Kraemer

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2023

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Semi-supervised Learning for Quality Estimation of Machine Translation
Tarun Bhatia | Martin Kraemer | Eduardo Vellasques | Eleftherios Avramidis
Proceedings of Machine Translation Summit XIX, Vol. 1: Research Track

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.
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