Zero-shot Dependency Parsing with Unsupervised Cross-Lingual Bootstrapping

Abstract:Pre-trained language models (PLMs) with encoder-based architectures have shown impressive capabilities in zero-shot cross-lingual transfer for various language understanding tasks. However, applying this technique to dependency parsing remains a significant challenge due to its syntactic nature. To boost model generalizability across linguistic typologies, we propose a cross-lingual unsupervised bootstrapping method to improve syntactic knowledge within the PLM. We show that our method achieves a significant improvement in zero-shot parsing performance in low-resource languages. Analysis of these bootstrapped models uncovers increased robustness in recognizing syntactic structures, evidenced by higher scores in parameter-free tree probing tests.

Computation and Language (cs.CL)

arXiv:2609.37883 [cs.CL]

(or arXiv:2609.37883v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.37883 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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