Adapting a FrameNet Semantic Parser for Spoken Language Understanding using Adversarial Learning
Table 5: Argument Identification results on the PODCAST-ASR corpus according to complexity factors (Fmax)
['D3', '[ITALIC] biGRU', '[ITALIC] biGRU+ [ITALIC] adv']
We focus now on the Argument Identification (AI) level. We try to identify precisely in which situations the adversarial learning strategy improves AI. This is not surprising since non-core FEs are often shared across several Frames despite having non trivial lexical variations for each Frame/domain. Consider the non-core FE Place whose content can vary from names of countries and cities to common nouns (”in the park”) and adverbs (”there”). Under these circumstances adversarial learning can successfully build a higher level representation of the FE. As for the other complexity factors, bigger gains are observed for the difficult conditions (i.e. nominal triggers and long sentences). On the other hand, adversarial learning slightly harmed performances on the short sentences.