Cross-Lingual Syntactic Transfer through Unsupervised Adaptation of Invertible Projections
Table 2: POS tagging accuracy results (%). Numbers next to languages names are their distances to English. Supervised accuracy on English (∗) is included for reference.
['[BOLD] Lang', 'Discriminative  [BOLD] LSTM-CRF', 'Generative  [BOLD] Flow-Fix', 'Generative  [BOLD] Flow-FT', 'Generative  [BOLD] Linear-FT']
When compared the discriminative LSTM-CRF baseline, our approach outperforms it on 8 out of 10 distant languages, with an average of 5.2% absolute improvement. Unsurprisingly, however, it also underperforms the expressive LSTM-CRF on 8 out of 10 nearby languages. The reasons for this phenomenon are two-fold. First, the flexible LSTM-CRF model is better able to fit the source English corpus than our method (94.02% vs 87.03% accuracy), thus it is also capable of fitting similar input when transferring. Second, unsupervised adaptation helps less when transferring to nearby languages (5.9% improvement over Flow-Fix versus 11.3% on distant languages), we posit that this is because a large portion of linguistic knowledge is shared between similar languages, and the cross-lingual word embeddings have better quality in this case, so unsupervised adaptation becomes less necessary. While the Linear-FT baseline on nearby languages is comparable to our method, its performance on distant languages is much worse, which confirms the importance of invertible projection, especially when language typologies are divergent.