  Most of the existing spoken language understanding systems can perform only
semantic frame parsing based on a single-round user query. They cannot take
users' feedback to update/add/remove slot values through multiround
interactions with users. In this paper, we introduce a novel multi-step spoken
language understanding system based on adversarial learning that can leverage
the multiround user's feedback to update slot values. We perform two
experiments on the benchmark ATIS dataset and demonstrate that the new system
can improve parsing performance by at least $2.5\%$ in terms of F1, with only
one round of feedback. The improvement becomes even larger when the number of
feedback rounds increases. Furthermore, we also compare the new system with
state-of-the-art dialogue state tracking systems and demonstrate that the new
interactive system can perform better on multiround spoken language
understanding tasks in terms of slot- and sentence-level accuracy.
