Abstract
Pre-training models on vast quantities of unlabeled data has emerged as an effective approach to improving accuracy on many NLP tasks. On the other hand, traditional machine translation has a long history of leveraging unlabeled data through noisy channel modeling. The same idea has recently been shown to achieve strong improvements for neural machine translation. Unfortunately, na ̈ıve noisy channel modeling with modern sequence to sequence models is up to an order of magnitude slower than alternatives. We address this issue by introducing efficient approximations to make inference with the noisy channel approach as fast as strong ensembles while increasing accuracy. We also show that the noisy channel approach can outperform strong pre-training results by achieving a new state of the art on WMT Romanian-English translation.- Anthology ID:
- 2020.wmt-1.69
- Volume:
- Proceedings of the Fifth Conference on Machine Translation
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 584–593
- Language:
- URL:
- https://aclanthology.org/2020.wmt-1.69
- DOI:
- Cite (ACL):
- Shruti Bhosale, Kyra Yee, Sergey Edunov, and Michael Auli. 2020. Language Models not just for Pre-training: Fast Online Neural Noisy Channel Modeling. In Proceedings of the Fifth Conference on Machine Translation, pages 584–593, Online. Association for Computational Linguistics.
- Cite (Informal):
- Language Models not just for Pre-training: Fast Online Neural Noisy Channel Modeling (Bhosale et al., WMT 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.wmt-1.69.pdf
- Code
- pytorch/fairseq
- Data
- WMT 2016, WMT 2016 News