Cross-Lingual Syntactic Transfer through Unsupervised Adaptation of Invertible Projections
Table 4: p-value of different distance features on POS tagging and dependency parsing task. A lower p-value indicates stronger association between the feature and the response, which is the difference between our method and the discriminative baselines.
['Feature', '[ITALIC] p-value POS tagging', '[ITALIC] p-value Dependency Parsing']
Specifically, we fit a generalized linear model (GLM) on the difference in accuracy and five features of all 20 target languages, then we perform a hypothesis test to compute the p-value that reflects the significance of specific features. As assumed before, inventory and phonological distance do not have much influence on the transfer. Interestingly, syntactic distance is not the significant term for both tasks, we posit that this is because the transfer performance is affected by both cross-lingual word embedding quality and linguistic features, thus genetic/geographic distance might be a better indicator overall. The results suggest that our method might be more suitable than the discriminative approach at genetically distant transfer for POS tagging and geographically distant transfer for parsing.