Semantic Role Labeling with Iterative Structure Refinement
Table 2: Number of sentences and predicates in training set of different languages.
['[EMPTY]', '#sent', '#pred', '#pred/#sent']
Datasets We conduct experiments on CoNLL-2009 Hajič et al. We use the predicted part-of-speech tags, dependency labels, and pre-identified predicate, provided with the dataset. As expected, weight tying is beneficial for lower-resource languages such as Catalan, Japanese and Spanish (see The Gumbel noise helps for all languages except for Czech and English, the two largest datasets. In particular, we observe almost no improvement on the Spanish dataset without using the Gumbel noise. We note relatively consistent but small gains from using 2 refinement iterations. The magnitude of the gains may be an artifact of us having the loss terms L(R∗,Rt) and L(P∗,Pt), encouraging not only the final (second), but also the first, iteration to produce accurate predictions. A potential alternative explanation is that our refinement network is restricted to simple interactions, resulting in the fixed point reachable in one step.