Abstract
In current research, most tree-based translation models are built directly from parse trees. In this study, we go in another direction and build a translation model with an unsupervised tree structure derived from a novel non-parametric Bayesian model. In the model, we utilize synchronous tree substitution grammars (STSG) to capture the bilingual mapping between language pairs. To train the model efficiently, we develop a Gibbs sampler with three novel Gibbs operators. The sampler is capable of exploring the infinite space of tree structures by performing local changes on the tree nodes. Experimental results show that the string-to-tree translation system using our Bayesian tree structures significantly outperforms the strong baseline string-to-tree system using parse trees.- Anthology ID:
- Q13-1020
- Volume:
- Transactions of the Association for Computational Linguistics, Volume 1
- Month:
- Year:
- 2013
- Address:
- Cambridge, MA
- Editors:
- Dekang Lin, Michael Collins
- Venue:
- TACL
- SIG:
- Publisher:
- MIT Press
- Note:
- Pages:
- 243–254
- Language:
- URL:
- https://aclanthology.org/Q13-1020
- DOI:
- 10.1162/tacl_a_00224
- Cite (ACL):
- Feifei Zhai, Jiajun Zhang, Yu Zhou, and Chengqing Zong. 2013. Unsupervised Tree Induction for Tree-based Translation. Transactions of the Association for Computational Linguistics, 1:243–254.
- Cite (Informal):
- Unsupervised Tree Induction for Tree-based Translation (Zhai et al., TACL 2013)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/Q13-1020.pdf