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:
- 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)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2023.mtsummit-research.7.pdf