A Novel Bi-directional Interrelated Model for Joint Intent Detection and Slot Filling

TL;DR

This paper introduces a novel bi-directional interrelated model for joint intent detection and slot filling in spoken language understanding, enhancing mutual promotion between tasks through a new iteration mechanism and direct connections.

Contribution

It proposes a new bi-directional interrelated model with an iteration mechanism that improves joint intent detection and slot filling performance.

Findings

Achieved 3.79% and 5.42% improvements in sentence-level accuracy on ATIS and Snips datasets.

Established direct bi-directional connections between intent detection and slot filling.

Enhanced mutual promotion between tasks leading to better SLU performance.

Abstract

A spoken language understanding (SLU) system includes two main tasks, slot filling (SF) and intent detection (ID). The joint model for the two tasks is becoming a tendency in SLU. But the bi-directional interrelated connections between the intent and slots are not established in the existing joint models. In this paper, we propose a novel bi-directional interrelated model for joint intent detection and slot filling. We introduce an SF-ID network to establish direct connections for the two tasks to help them promote each other mutually. Besides, we design an entirely new iteration mechanism inside the SF-ID network to enhance the bi-directional interrelated connections. The experimental results show that the relative improvement in the sentence-level semantic frame accuracy of our model is 3.79% and 5.42% on ATIS and Snips datasets, respectively, compared to the state-of-the-art model.