Cross-Lingual Syntactic Transfer through Unsupervised Adaptation of Invertible Projections
Table 3: Dependency parsing UAS (%) on sentences of all lengths. Numbers next to languages names are their distances to English. Supervised accuracy on English (∗) is included for reference.
['[BOLD] Lang', 'Discriminative  [BOLD] SelfAtt-Graph', 'Generative  [BOLD] Flow-Fix', 'Generative  [BOLD] Flow-FT']
While unsupervised fine-tuning improves the performance on the distant languages, it only has minimal effect on nearby languages, which is consistent with our observations in the POS tagging experiment and implies that unsupervised adaption helps more for distant transfer. Similar to POS tagging results, our method is able to outperform state-of-the-art “SelfAtt-Graph” model on 8 out of 10 distant languages, with an average of 8.3% absolute improvement, but the strong discriminative baseline performs better when transferring to nearby languages. Note that the supervised performance of our method on English is poor. This is mainly because the DMV prior is too simple and limits the capacity of the model. While this model still achieves good performance on distant transfer, incorporating more complex DMV variants (Jiang et al.,