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.