Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling

TL;DR

This paper introduces an attention-based neural network model for joint intent detection and slot filling, improving accuracy by leveraging attention mechanisms in encoder-decoder frameworks for speech understanding tasks.

Contribution

It proposes a novel attention-based joint model that enhances intent detection and slot filling accuracy, outperforming previous independent models on the ATIS benchmark.

Findings

Achieved state-of-the-art intent detection error rate.

Improved slot filling F1 score.

Reduced intent detection error by 23.8% relative.

Abstract

Attention-based encoder-decoder neural network models have recently shown promising results in machine translation and speech recognition. In this work, we propose an attention-based neural network model for joint intent detection and slot filling, both of which are critical steps for many speech understanding and dialog systems. Unlike in machine translation and speech recognition, alignment is explicit in slot filling. We explore different strategies in incorporating this alignment information to the encoder-decoder framework. Learning from the attention mechanism in encoder-decoder model, we further propose introducing attention to the alignment-based RNN models. Such attentions provide additional information to the intent classification and slot label prediction. Our independent task models achieve state-of-the-art intent detection error rate and slot filling F1 score on the benchmark ATIS task. Our joint training model further obtains 0.56% absolute (23.8% relative) error reduction on intent detection and 0.23% absolute gain on slot filling over the independent task models.