Multilingual Zero-shot Constituency Parsing
Table 4: Recall scores on gold-standard noun and verb phrases with the corresponding sentence-level F1 scores. We observe that pre-training grants the XLM-R model an ability to identify noun and verb phrases at an intermediate level.
['[BOLD] Measure / Language', '[BOLD] en', '[BOLD] fr', '[BOLD] de', '[BOLD] he', '[BOLD] ko', '[BOLD] sv']
Interestingly, this effect appears uniform irrespective of input language, offering a consistent amount of gain (about 30 percent) on the NP recall scores for all languages when compared with the results of the random baseline. However, the XLM-R model shows fluctuating performance with regard to recognizing verb phrases; we obtain the biggest gains for German and Swedish. This is an interesting, though not surprising result, as German and Swedish are V2, or verb-second languages, meaning that the location of the verb in a sentence is highly predictable, and suggests that the model is sensitive to this generalization.