Abstract
This paper introduces two novel approaches for extracting compact grammars for hierarchical phrase-based translation. The first is a combinatorial optimization approach and the second is a Bayesian model over Hiero grammars using Variational Bayes for inference. In contrast to the conventional Hiero (Chiang, 2007) rule extraction algorithm , our methods extract compact models reducing model size by 17.8% to 57.6% without impacting translation quality across several language pairs. The Bayesian model is particularly effective for resource-poor languages with evidence from Korean-English translation. To our knowledge, this is the first alternative to Hiero-style rule extraction that finds a more compact synchronous grammar without hurting translation performance.- Anthology ID:
- 2012.amta-papers.16
- Volume:
- Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Research Papers
- Month:
- October 28-November 1
- Year:
- 2012
- Address:
- San Diego, California, USA
- Venue:
- AMTA
- SIG:
- Publisher:
- Association for Machine Translation in the Americas
- Note:
- Pages:
- Language:
- URL:
- https://aclanthology.org/2012.amta-papers.16
- DOI:
- Cite (ACL):
- Baskaran Sankaran, Gholamreza Haffari, and Anoop Sarkar. 2012. Compact Rule Extraction for Hierarchical Phrase-based Translation. In Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Research Papers, San Diego, California, USA. Association for Machine Translation in the Americas.
- Cite (Informal):
- Compact Rule Extraction for Hierarchical Phrase-based Translation (Sankaran et al., AMTA 2012)
- PDF:
- https://preview.aclanthology.org/bionlp-24-ingestion/2012.amta-papers.16.pdf