Semi-supervised Learning for Quality Estimation of Machine Translation

Tarun Bhatia, Martin Kraemer, Eduardo Vellasques, Eleftherios Avramidis


Abstract
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.
Anthology ID:
2023.mtsummit-research.7
Volume:
Proceedings of Machine Translation Summit XIX, Vol. 1: Research Track
Month:
September
Year:
2023
Address:
Macau SAR, China
Editors:
Masao Utiyama, Rui Wang
Venue:
MTSummit
SIG:
Publisher:
Asia-Pacific Association for Machine Translation
Note:
Pages:
72–83
Language:
URL:
https://aclanthology.org/2023.mtsummit-research.7
DOI:
Bibkey:
Cite (ACL):
Tarun Bhatia, Martin Kraemer, Eduardo Vellasques, and Eleftherios Avramidis. 2023. Semi-supervised Learning for Quality Estimation of Machine Translation. In Proceedings of Machine Translation Summit XIX, Vol. 1: Research Track, pages 72–83, Macau SAR, China. Asia-Pacific Association for Machine Translation.
Cite (Informal):
Semi-supervised Learning for Quality Estimation of Machine Translation (Bhatia et al., MTSummit 2023)
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PDF:
https://aclanthology.org/2023.mtsummit-research.7.pdf