Robust Semantic Parsing with Adversarial Learning for Domain Generalization
Table 5: Frame Element Identification results according to complexity factors on D3 (Fmax)
['D3', '[ITALIC] biGRU', '[ITALIC] biGRU+ [ITALIC] AC']
Argument identification In this section, we focus on the Frame Argument (or FE for Frame Element) Identification level, and propose contrastive experiments following the complexity factors analysis proposed by Marzinotto et al. In this study, the authors have shown that FE identification is performing better for verbal LU triggers than for nominal ones, and for triggers that are at the root of the syntactic dependency tree. We want to see here how these complexity factors are affected by the adversarial learning strategy. Additionally, we want to see if the system behaves equivalently over core and non-core Frame Elements. Actually, in the usual evaluation Framework of the Framenet 1.5 shared task, non-core FEs are assigned a 0.5 weight for the F-measure computation, reducing the impact of errors on non-core FEs. In this paper, all FEs are rated equally, but here we separate them to observe their behaviour. Moreover, bigger gains are observed for the difficult conditions.