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arXiv · 1909.06775

Cross-Lingual BERT Transformation for Zero-Shot Dependency Parsing

Yuxuan Wang·Wanxiang Che·Jiang Guo·Yijia Liu·Ting Liu
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Abstract

This paper investigates the problem of learning cross-lingual representations in a contextual space. We propose Cross-Lingual BERT Transformation (CLBT), a simple and efficient approach to generate cross-lingual contextualized word embeddings based on publicly available pre-trained BERT models (Devlin et al., 2018). In this approach, a linear transformation is learned from contextual word alignments to align the contextualized embeddings independently trained in different languages. We demonstrate the effectiveness of this approach on zero-shot cross-lingual transfer parsing. Experiments show that our embeddings substantially outperform the previous state-of-the-art that uses static embeddings. We further compare our approach with XLM (Lample and Conneau, 2019), a recently proposed cross-lingual language model trained with massive parallel data, and achieve highly competitive results.

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Yuxuan Wang, Wanxiang Che, Jiang Guo, Yijia Liu, Ting Liu. 2019-09-15. Cross-Lingual BERT Transformation for Zero-Shot Dependency Parsing. https://arxiv.org/abs/1909.06775

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