  Most recent semantic frame parsing systems for spoken language understanding
(SLU) are designed based on recurrent neural networks. These systems display
decent performance on benchmark SLU datasets such as ATIS or SNIPS, which
contain short utterances with relatively simple patterns. However, the current
semantic frame parsing models lack a mechanism to handle out-of-distribution
(\emph{OOD}) patterns and out-of-vocabulary (\emph{OOV}) tokens. In this paper,
we introduce a robust semantic frame parsing pipeline that can handle both
\emph{OOD} patterns and \emph{OOV} tokens in conjunction with a new complex
Twitter dataset that contains long tweets with more \emph{OOD} patterns and
\emph{OOV} tokens. The new pipeline demonstrates much better results in
comparison to state-of-the-art baseline SLU models on both the SNIPS dataset
and the new Twitter dataset (Our new Twitter dataset can be downloaded from
https://1drv.ms/u/s!AroHb-W6_OAlavK4begsDsMALfE?e=c8f2XX ). Finally, we also
build an E2E application to demo the feasibility of our algorithm and show why
it is useful in real application.
