  Popular approaches for Natural Language Understanding (NLU) usually rely on a
huge amount of annotated data or handcrafted rules, which is laborious and not
adaptive to domain extension. We recently proposed a
Convex-Polytopic-Model-based framework that shows great potential in
automatically extracting semantic patterns by exploiting the raw dialog corpus.
The extracted semantic patterns can be used to generate semantic frames, which
is essential in assisting NLU tasks. This paper further studies the CPM model
in depth and visualizes its high interpretability and transparency at various
levels. We show that this framework can exploit
  semantic-frame-related features in the corpus, reveal the underlying semantic
structure of the utterances, and boost the performance of the state-of-the-art
NLU model with minimal supervision. We conduct our experiments on the ATIS (Air
Travel Information System) corpus.
