  Spoken language understanding (SLU) is an essential component in
conversational systems. Considering that contexts provide informative cues for
better understanding, history can be leveraged for contextual SLU. However,
most prior work only paid attention to the related content in history
utterances and ignored the temporal information. In dialogues, it is intuitive
that the most recent utterances are more important than the least recent ones,
and time-aware attention should be in a decaying manner. Therefore, this paper
allows the model to automatically learn a time-decay attention function where
the attentional weights can be dynamically decided based on the content of each
role's contexts, which effectively integrates both content-aware and time-aware
perspectives and demonstrates remarkable flexibility to complex dialogue
contexts. The experiments on the benchmark Dialogue State Tracking Challenge
(DSTC4) dataset show that the proposed dynamically context-sensitive time-decay
attention mechanisms significantly improve the state-of-the-art model for
contextual understanding performance.
