Abstract
The process of translation is ambiguous, in that there are typically many valid translations for a given sentence. This gives rise to significant variation in parallel corpora, however, most current models of machine translation do not account for this variation, instead treating the problem as a deterministic process. To this end, we present a deep generative model of machine translation which incorporates a chain of latent variables, in order to account for local lexical and syntactic variation in parallel corpora. We provide an in-depth analysis of the pitfalls encountered in variational inference for training deep generative models. Experiments on several different language pairs demonstrate that the model consistently improves over strong baselines.- Anthology ID:
- P18-1115
- Volume:
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Editors:
- Iryna Gurevych, Yusuke Miyao
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1243–1252
- Language:
- URL:
- https://aclanthology.org/P18-1115
- DOI:
- 10.18653/v1/P18-1115
- Cite (ACL):
- Philip Schulz, Wilker Aziz, and Trevor Cohn. 2018. A Stochastic Decoder for Neural Machine Translation. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1243–1252, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- A Stochastic Decoder for Neural Machine Translation (Schulz et al., ACL 2018)
- PDF:
- https://preview.aclanthology.org/naacl24-info/P18-1115.pdf
- Code
- awslabs/sockeye