Context-Aware Graph Segmentation for Graph-Based Translation

Liangyou Li, Andy Way, Qun Liu


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:
Bibkey:
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)
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PDF:
https://preview.aclanthology.org/ml4al-ingestion/E17-2095.pdf