This paper proposes to learn languageindependent word representations to address 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 language. 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 similarities of words not only within the same language 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 investigate the effectiveness of the proposed technique, extensive experiments are conducted on cross-lingual dependency parsing 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 alternative comparison methods.
Recently, REF used a set of bilingual word pairs as pivots to learn interlingual distributed word representations via deep neural networks as augmenting features for cross-lingual dependency parsing.
Distributed Word Representation Learning for Cross-Lingual Dependency Parsing
CoNLL
Computer Science