Learning Compact Metrics for MT
Amy Pu, Hyung Won Chung, Ankur Parikh, Sebastian Gehrmann, Thibault Sellam
Abstract
Recent developments in machine translation and multilingual text generation have led researchers to adopt trained metrics such as COMET or BLEURT, which treat evaluation as a regression problem and use representations from multilingual pre-trained models such as XLM-RoBERTa or mBERT. Yet studies on related tasks suggest that these models are most efficient when they are large, which is costly and impractical for evaluation. We investigate the trade-off between multilinguality and model capacity with RemBERT, a state-of-the-art multilingual language model, using data from the WMT Metrics Shared Task. We present a series of experiments which show that model size is indeed a bottleneck for cross-lingual transfer, then demonstrate how distillation can help addressing this bottleneck, by leveraging synthetic data generation and transferring knowledge from one teacher to multiple students trained on related languages. Our method yields up to 10.5% improvement over vanilla fine-tuning and reaches 92.6% of RemBERT’s performance using only a third of its parameters.- Anthology ID:
- 2021.emnlp-main.58
- Original:
- 2021.emnlp-main.58v1
- Version 2:
- 2021.emnlp-main.58v2
- Volume:
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Month:
- November
- Year:
- 2021
- Address:
- Online and Punta Cana, Dominican Republic
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 751–762
- Language:
- URL:
- https://aclanthology.org/2021.emnlp-main.58
- DOI:
- 10.18653/v1/2021.emnlp-main.58
- Cite (ACL):
- Amy Pu, Hyung Won Chung, Ankur Parikh, Sebastian Gehrmann, and Thibault Sellam. 2021. Learning Compact Metrics for MT. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 751–762, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Learning Compact Metrics for MT (Pu et al., EMNLP 2021)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2021.emnlp-main.58.pdf
- Code
- google-research/bleurt
- Data
- C4, mC4