Prior Knowledge Integration for Neural Machine Translation using Posterior Regularization
Jiacheng Zhang, Yang Liu, Huanbo Luan, Jingfang Xu, Maosong Sun
Abstract
Although neural machine translation has made significant progress recently, how to integrate multiple overlapping, arbitrary prior knowledge sources remains a challenge. In this work, we propose to use posterior regularization to provide a general framework for integrating prior knowledge into neural machine translation. We represent prior knowledge sources as features in a log-linear model, which guides the learning processing of the neural translation model. Experiments on Chinese-English dataset show that our approach leads to significant improvements.- Anthology ID:
- P17-1139
- Volume:
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Regina Barzilay, Min-Yen Kan
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1514–1523
- Language:
- URL:
- https://aclanthology.org/P17-1139
- DOI:
- 10.18653/v1/P17-1139
- Cite (ACL):
- Jiacheng Zhang, Yang Liu, Huanbo Luan, Jingfang Xu, and Maosong Sun. 2017. Prior Knowledge Integration for Neural Machine Translation using Posterior Regularization. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1514–1523, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Prior Knowledge Integration for Neural Machine Translation using Posterior Regularization (Zhang et al., ACL 2017)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/P17-1139.pdf
- Code
- Glaceon31/PR4NMT