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Dependency Graph-to-String Translation
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing
Compared to tree grammars, graph grammars have stronger generative capacity over structures. Based on an edge replacement grammar, in this paper we propose to use a synchronous graph-to-string grammar for statistical machine translation. The graph we use is directly converted from a dependency tree by labelling edges. We build our translation model in the log-linear framework with standard features. Large-scale experiments on Chinese-English and German-English tasks show that our model isdoi:10.18653/v1/d15-1004 dblp:conf/emnlp/LiWL15 fatcat:474upobprre57fxon6sbiz6pia