Distributed Word Representation Learning for Cross-Lingual Dependency Parsing

By Min Xiao, Yuhong Guo. 2014

102 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

XNLI: Evaluating Cross-lingual Sentence Representations (2018, background): “(2016) show that cross-lingual embeddings can be used to extend an English part-of-speech tagger to the cross-lingual setting, and Xiao and Guo (2014) achieve similar results in dependency parsing.”
Learning bilingual word embeddings with (almost) no bilingual data (2017, background): “…2016), they provide an excellent mechanism for transfer learning, where a model trained in a resource-rich language is transferred to a less-resourced one, as shown with part-of-speech tagging (Zhang et al., 2016), parsing (Xiao and Guo, 2014) and document classiﬁcation (Klementiev et al., 2012).”
A Survey of Cross-lingual Word Embedding Models (2017, methodology): “This completes the proof that retro-fitting of SGNS vector spaces and Constrained Bilingual SGNS are equivalent when α = Ω∞.”
Unsupervised Cross-lingual Word Embedding by Multilingual Neural Language Models (2018, background): “It also enables the transfer of knowledge from one language into another (Xiao and Guo 2014; Adams et al. 2017).”
Ten Pairs to Tag – Multilingual POS Tagging via Coarse Mapping between Embeddings (2016, background): “, 2015), syntactic parsing (Xiao and Guo, 2014; Guo et al., 2015; Durrett et al., 2012), and machine translation (Zou et al.”