Cross-Lingual Dependency Parsing Using Code-Mixed TreeBank
Table 3: Final results.
['Lang.', 'Delex UAS', 'Delex LAS', 'PartProj UAS', 'PartProj LAS', 'Src UAS', 'Src LAS', 'Tgt UAS', 'Tgt LAS', 'Src + Tgt UAS', 'Src + Tgt LAS', 'Mix UAS', 'Mix LAS', 'Src + Mix UAS', 'Src + Mix LAS']
As shown, the model Tgt gives better averaged performance compared to Src. However, its results on French and Italian are slightly worse, which indicates that noise from translation impacts the quality of the projected treebank. The model Mix gives much better performance compared with Src, demonstrating the effectiveness of structural transfer. Mix also outperforms Tgt significantly, obtaining average increases of 80.93−76.37=4.56 points on UAS and 72.87−68.28=4.59 points on LAS, respectively. The best setting is the model Src + Mix, trained on the combined corpus of the source and code-mixed treebanks, which gives better performance than solely code-mixed treebanks. By comparing with the delexicalized model Delex, we can see that lexicalized features are highly useful for cross-lingual transfer. For the PartProj model, we conduct preliminary experiments on Swedish to tune the ratio of the projected dependencies. The results show that the difference is very small (δ=0.24 for UAS) between 0.9 to 1.0, and the performance degrades significantly as the ratio decreases below 0.9. The observation indicates that this method is probably not effective for filtering low-confidence word alignments. The final results confirm our hypothesis. One possible reason may be the unremoved target words (if the words are removed, the PartProj model with ratio 1.0 will be identical to Tgt), which have been demonstrated noisy previously tiedemann2016synthetic.