Bi-directional Joint Neural Networks for Intent Classification and Slot Filling

TL;DR

This paper introduces a bi-directional joint neural network model that leverages BERT and mutual intent-slot mechanisms to improve intent classification and slot filling in natural language understanding, achieving state-of-the-art results.

Contribution

The paper presents a novel bi-directional joint model with hierarchical processing and mutual intent-slot mechanisms, enhancing performance over previous models.

Findings

Achieved 88.6% accuracy on ATIS dataset.

Achieved 92.8% accuracy on SNIPS dataset.

Significantly improved sentence-level semantic frame accuracy.

Abstract

Intent classification and slot filling are two critical tasks for natural language understanding. Traditionally the two tasks proceeded independently. However, more recently joint models for intent classification and slot filling have achieved state-of-the-art performance, and have proved that there exists a strong relationship between the two tasks. In this paper, we propose a bi-directional joint model for intent classification and slot filling, which includes a multi-stage hierarchical process via BERT and bi-directional joint natural language understanding mechanisms, including intent2slot and slot2intent, to obtain mutual performance enhancement between intent classification and slot filling. The evaluations show that our model achieves state-of-the-art results on intent classification accuracy, slot filling F1, and significantly improves sentence-level semantic frame accuracy when applied to publicly available benchmark datasets, ATIS (88.6%) and SNIPS (92.8%).