Abstract
We propose the use of pre-trained embeddings as features of a regression model for sentence-level quality estimation of machine translation. In our work we combine freely available BERT and LASER multilingual embeddings to train a neural-based regression model. In the second proposed method we use as an input features not only pre-trained embeddings, but also log probability of any machine translation (MT) system. Both methods are applied to several language pairs and are evaluated both as a classical quality estimation system (predicting the HTER score) as well as an MT metric (predicting human judgements of translation quality).- Anthology ID:
- W19-5410
- Volume:
- Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)
- Month:
- August
- Year:
- 2019
- Address:
- Florence, Italy
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 101–105
- Language:
- URL:
- https://aclanthology.org/W19-5410
- DOI:
- 10.18653/v1/W19-5410
- Cite (ACL):
- Elizaveta Yankovskaya, Andre Tättar, and Mark Fishel. 2019. Quality Estimation and Translation Metrics via Pre-trained Word and Sentence Embeddings. In Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pages 101–105, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Quality Estimation and Translation Metrics via Pre-trained Word and Sentence Embeddings (Yankovskaya et al., WMT 2019)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/W19-5410.pdf