Abstract
Quality estimation (QE) is the task of predicting quality of outputs produced by machine translation (MT) systems. Currently, the highest-performing QE systems are supervised and require training on data with golden quality scores. In this paper, we investigate the impact of the quality of the underlying MT outputs on the performance of QE systems. We find that QE models trained on datasets with lower-quality translations often outperform those trained on higher-quality data. We also demonstrate that good performance can be achieved by using a mix of data from different MT systems.- Anthology ID:
- 2023.mtsummit-research.17
- 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:
- 200–208
- Language:
- URL:
- https://aclanthology.org/2023.mtsummit-research.17
- DOI:
- Cite (ACL):
- Iryna Tryhubyshyn, Aleš Tamchyna, and Ondřej Bojar. 2023. Bad MT Systems are Good for Quality Estimation. In Proceedings of Machine Translation Summit XIX, Vol. 1: Research Track, pages 200–208, Macau SAR, China. Asia-Pacific Association for Machine Translation.
- Cite (Informal):
- Bad MT Systems are Good for Quality Estimation (Tryhubyshyn et al., MTSummit 2023)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2023.mtsummit-research.17.pdf