A Joint Multi-Task Learning Framework for Spoken Language Understanding

Spoken language understanding (SLU), which mainly involves intent prediction and slot filling, is a core component of a spoken dialogue system. Usually, intent determination and slot filling are carried out independently. Recently, joint learning of intent determination and slot filling has been proved effective in SLU. In this paper, we propose a novel joint multi-task learning framework for SLU, which predicts user intent and slot label via shared LSTM architecture, as well as next word's part of speech (POS) via neural language model. The proposed model exploits the correlation among different tasks and makes full advantage of all supervised signals. We conduct experiments on popular benchmark ATIS dataset, which consists of rich dialogues collected from real world. The experiment results show that our model achieves state-of-the-art in terms of several popular metrics.