Abstract
Most studies on word-level Quality Estimation (QE) of machine translation focus on language-specific models. The obvious disadvantages of these approaches are the need for labelled data for each language pair and the high cost required to maintain several language-specific models. To overcome these problems, we explore different approaches to multilingual, word-level QE. We show that multilingual QE models perform on par with the current language-specific models. In the cases of zero-shot and few-shot QE, we demonstrate that it is possible to accurately predict word-level quality for any given new language pair from models trained on other language pairs. Our findings suggest that the word-level QE models based on powerful pre-trained transformers that we propose in this paper generalise well across languages, making them more useful in real-world scenarios.- Anthology ID:
- 2021.acl-short.55
- Volume:
- Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Editors:
- Chengqing Zong, Fei Xia, Wenjie Li, Roberto Navigli
- Venues:
- ACL | IJCNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 434–440
- Language:
- URL:
- https://aclanthology.org/2021.acl-short.55
- DOI:
- 10.18653/v1/2021.acl-short.55
- Cite (ACL):
- Tharindu Ranasinghe, Constantin Orasan, and Ruslan Mitkov. 2021. An Exploratory Analysis of Multilingual Word-Level Quality Estimation with Cross-Lingual Transformers. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 434–440, Online. Association for Computational Linguistics.
- Cite (Informal):
- An Exploratory Analysis of Multilingual Word-Level Quality Estimation with Cross-Lingual Transformers (Ranasinghe et al., ACL-IJCNLP 2021)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2021.acl-short.55.pdf
- Code
- tharindudr/transQuest