A Bidirectional Joint Model for Spoken Language Understanding

Abstract

Intent detection and slot filling are two fundamental and important tasks in spoken language understanding (SLU). Motivated by the fact that the intent and slots in a user utterance have a strong relationship, joint models that deal with both tasks in a single framework have become a predominant choice in SLU research. Most existing joint models build two different decoders on top of a shared weight encoder or exploit intent information to detect slots. Some joint models transfer information between two tasks implicitly. In this paper, we propose a bidirectional joint model for SLU that explicitly incorporates intent information into slot filling and slot information into intent detection. Specifically, we first predict a soft intent signal, which is fed into a biaffine classifier to recognize slots. Slot features are then employed along with the utterance representation to predict the final intent. We also introduce a loss function that takes into account three types of losses: soft intent detection, final intent detection, and slot filling. Experimental results on three benchmark datasets ATIS, Snips, and PhoATIS show that our model outperforms previous state-of-the-art models in both tasks with relative error reductions ranging from 6% to 22%.