Abstract
In this paper, we present an improved graph-based translation model which segments an input graph into node-induced subgraphs by taking source context into consideration. Translations are generated by combining subgraph translations left-to-right using beam search. Experiments on Chinese–English and German–English demonstrate that the context-aware segmentation significantly improves the baseline graph-based model.- Anthology ID:
- E17-2095
- Volume:
- Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
- Month:
- April
- Year:
- 2017
- Address:
- Valencia, Spain
- Editors:
- Mirella Lapata, Phil Blunsom, Alexander Koller
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 599–604
- Language:
- URL:
- https://aclanthology.org/E17-2095
- DOI:
- Cite (ACL):
- Liangyou Li, Andy Way, and Qun Liu. 2017. Context-Aware Graph Segmentation for Graph-Based Translation. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 599–604, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- Context-Aware Graph Segmentation for Graph-Based Translation (Li et al., EACL 2017)
- PDF:
- https://preview.aclanthology.org/ml4al-ingestion/E17-2095.pdf