Distributed Word Representation Learning for Cross-Lingual Dependency Parsing
Min Xiao, Yuhong Guo · 2014
This paper proposes to learn language-independent word representations to ad-dress cross-lingual dependency parsing, which aims to predict the dependency parsing trees for sentences in the target language by training a dependency parser with labeled sentences from a source lan-guage. We first combine all sentences from both languages to induce real-valued distributed representation of words under a deep neural network architecture, which is expected to capture semantic similari-ties of words not only within the same lan-guage but also across different languages. We then use the induced interlingual word representation as augmenting features to train a delexicalized dependency parser on labeled sentences in the source language and apply it to the target sentences. To in-vestigate the effectiveness of the proposed technique, extensive experiments are con-ducted on cross-lingual dependency pars-ing tasks with nine different languages. The experimental results demonstrate the superior cross-lingual generalizability of the word representation induced by the proposed approach, comparing to alterna-tive comparison methods. 1