Adapting a FrameNet Semantic Parser for Spoken Language Understanding using Adversarial Learning
Table 3: F-measure (Fmax) on FI and AI with (biGRU+adv) and without (biGRU) adversarial training.
['[EMPTY]', 'Frame Ident. PODCAST', 'Frame Ident. PODCAST', 'Argument Ident. PODCAST', 'Argument Ident. PODCAST']
For the sake of comparison, the results are also provided over the initial CALOR test set, showing that these results are not harmed by the adaptation process. The corpus with the highest improvements is the ASR corpus, with +2.5 points. This shows that our approach can help building higher level representation that are more independent from data source (written or spoken language) especially when dealing with transcription errors.