We present a new neural architecture for wide-coverage Natural Language Understanding in Spoken Dialogue Systems. We develop a hierarchical multi-task architecture, which delivers a multi-layer representation of sentence meaning (i.e., Dialogue Acts and Frame-like structures). The architecture is a hierarchy of self-attention mechanisms and BiLSTM encoders followed by CRF tagging layers. We describe a variety of experiments, showing that our approach obtains promising results on a dataset annotated with Dialogue Acts and Frame Semantics. Moreover, we demonstrate its applicability to a different, publicly available NLU dataset annotated with domain-specific intents and corresponding semantic roles, providing overall performance higher than state-of-the-art tools such as RASA, Dialogflow, LUIS, and Watson. For example, we show an average 4.45% improvement in entity tagging F-score over Rasa, Dialogflow and LUIS.

我们提出了一个用于口语对话系统宽覆盖自然语言理解的新型神经架构，该架构采用层次多任务结构，具有自我注意机制、BiLSTM编码器和CRF标签层。我们展示了该方法在对话行为和框架语义上获得了可喜的结果，并证明了其适用于注释为特定领域意图和相应语义角色的不同NLU数据集，整体表现优于RASA、Dialogflow、LUIS和Watson等现有工具。例如，我们展示了平均实体标记F分数比Rasa、Dialogflow和LUIS提高了4.45%。

跨领域会话AI的分层多任务自然语言理解：HERMIT NLU