A Representation Learning Framework for Multi Source Transfer Parsing
Cross lingual model transfer has been a promising approach for inducing dependency parsers for low resource 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 language specific 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 tree banks (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.
AAAI