An Interactive Two-Pass Decoding Network for Joint Intent Detection and Slot Filling

Abstract

Intent detection and slot filling are two closely related tasks for building a spoken language understanding (SLU) system. The joint methods for the two tasks focus on modeling the semantic correlations between the intent and slots and applying the information of one task to guide the other task, which helps them to promote each other. However, most existing joint approaches only unidirectionally utilize the intent information to guide slot filling while ignoring the fact that the slot information is beneficial to intent detection. To address this issue, in this paper, we propose an Interactive Two-pass Decoding Network (ITD-Net) for joint intent detection and slot filling, which explicitly establishes the token-level interactions between the intent and slots through performing an interactive two-pass decoding process. In ITD-Net, the task-specific information obtained by the first-pass decoder for one task is directly fed into the second-pass decoder for the other task, which can take full advantage of the explicit intent and slot information to achieve bidirectional guidance between the two tasks. Experiments on the ATIS and SNIPS datasets demonstrate the effectiveness and superiority of our ITD-Net.