A Representation Learning Framework for Multi-Source Transfer Parsing

Cross-lingual model transfer has been a promising approach for inducing dependency parsers for lowresource languages where annotated treebanks are not available. The major obstacles for the model transfer approach are two-fold: 1. Lexical features are not directly transferable across languages; 2. Target languagespecific syntactic structures are difficult to be recovered. To address these two challenges, we present a novel representation learning framework for multi-source transfer parsing. Our framework allows multi-source transfer parsing using full lexical features straightforwardly. By evaluating on the Google universal dependency treebanks (v2. 0), our best models yield an absolute improvement of 6. 53% in averaged labeled attachment score, as compared with delexicalized multi-source transfer models. We also significantly outperform the state-of-the-art transfer system proposed most recently.

Authors

• Jiang Guo
• Wanxiang Che Research Center for SCIR, Harbin Institute of Technology, Harbin, China
• David Yarowsky
• Haifeng Wang
• Ting Liu Tsinghua University

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