  Spoken Language Understanding (SLU) is an essential part of the spoken
dialogue system, which typically consists of intent detection (ID) and slot
filling (SF) tasks. Recently, recurrent neural networks (RNNs) based methods
achieved the state-of-the-art for SLU. It is noted that, in the existing
RNN-based approaches, ID and SF tasks are often jointly modeled to utilize the
correlation information between them. However, we noted that, so far, the
efforts to obtain better performance by supporting bidirectional and explicit
information exchange between ID and SF are not well studied.In addition, few
studies attempt to capture the local context information to enhance the
performance of SF. Motivated by these findings, in this paper, Parallel
Interactive Network (PIN) is proposed to model the mutual guidance between ID
and SF. Specifically, given an utterance, a Gaussian self-attentive encoder is
introduced to generate the context-aware feature embedding of the utterance
which is able to capture local context information. Taking the feature
embedding of the utterance, Slot2Intent module and Intent2Slot module are
developed to capture the bidirectional information flow for ID and SF tasks.
Finally, a cooperation mechanism is constructed to fuse the information
obtained from Slot2Intent and Intent2Slot modules to further reduce the
prediction bias.The experiments on two benchmark datasets, i.e., SNIPS and
ATIS, demonstrate the effectiveness of our approach, which achieves a
competitive result with state-of-the-art models. More encouragingly, by using
the feature embedding of the utterance generated by the pre-trained language
model BERT, our method achieves the state-of-the-art among all comparison
approaches.
