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
this https URL ). Finally, we also
build an E2E application to demo the feasibility of our algorithm and show why
it is useful in real application.

本研究介绍了一种稳健的语义框架解析系统，使用循环神经网络来解析口语理解情景下的复杂长文本，此系统可处理分布外模式和分布外词汇，并显示比当前最先进的基准 SLU 模型更好的结果。