Joint Inference for Heterogeneous Dependency Parsing
Guangyou Zhou and Jun Zhao
The 51st Annual Meeting of the Association for Computational Linguistics - Short Papers (ACL Short Papers 2013)
Sofia, Bulgaria, August 4-9, 2013
This paper is concerned with the problem of heterogeneous dependency parsing. In this paper, we present a novel joint infer- ence scheme, which is able to leverage the consensus information between het- erogeneous treebanks in the parsing phase. Different from stacked learning meth- ods (Nivre and McDonald, 2008; Martins et al., 2008), which process the depen- dency parsing in a pipelined way (e.g., a second level uses the first level outputs), in our method, multiple dependency parsing models are coordinated to exchange con- sensus information. We conduct experi- ments on Chinese Dependency Treebank (CDT) and Penn Chinese Treebank (CTB), experimental results show that joint infer- ence can bring significant improvements to all state-of-the-art dependency parsers.
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