Many Languages, One Parser
Table 5: Effect of automatically predicting language ID and POS tags with MaLOPa on LAS scores.
['LAS', 'language ID', 'coarse POS', 'target language de', 'target language en', 'target language es', 'target language fr', 'target language it', 'target language pt', 'target language sv', 'average']
The macro average language ID prediction accuracy on the test set across sentences is 94.7%. The macro average accuracy of the POS tagger is 93.3%. the four configurations: {gold language ID, predicted language ID} × {gold POS tags, predicted POS tags}. The performance of the parser suffers mildly (–0.8 LAS points) when using predicted language IDs, but more (–5.1 LAS points) when using predicted POS tags. The disparity in parsing results with gold vs. predicted POS tags is an important open problem, and has been previously discussed by \newcitetiedemann:15. Without using block dropout, we lose an extra 0.2 LAS points in both configurations using predicted POS tags.