Adapting a FrameNet Semantic Parser for Spoken Language Understanding using Adversarial Learning
Table 4: Frame Identification score for LUs with the highest variation of sense distribution across corpora
['LU', 'GOLD  [ITALIC] biGRU', 'GOLD + [ITALIC] adv', 'ASR  [ITALIC] biGRU', 'ASR + [ITALIC] adv']
We observed that most of the errors in Frame Identification are associated to LUs that are polysemous in a general context, but are unambiguous given a thematic domain. For example, dire (to say) triggers the frame Statement in most of the CALOR corpus, but in the PODCAST corpus most of the time it does not trigger any Frame as it is used as part of an oral expression. Under these circumstances, the model underestimates the FI task and assigns a Frame to a LU without doing a proper analysis of its meaning using appropriated features. These changes are mostly due to LUs used in oral expression, rather than to a thematic change. Adversarial training is beneficial for these LUs, specially for the ASR. This result supports the idea that adaptation can be useful even if the spoken and written thematic domains are similar.