Explainable Slot Type Attentions to Improve Joint Intent Detection and Slot Filling

TL;DR

This paper introduces an explainable joint intent detection and slot filling model that enhances accuracy by generating slot type specific features and provides explanations for its decisions without post-hoc methods.

Contribution

It proposes a novel method that learns to generate slot type specific features and offers explanations for slot filling decisions within a joint NLU model.

Findings

Improves accuracy on two datasets

Provides inherent explainability without post-hoc methods

Enhances slot filling interpretability

Abstract

Joint intent detection and slot filling is a key research topic in natural language understanding (NLU). Existing joint intent and slot filling systems analyze and compute features collectively for all slot types, and importantly, have no way to explain the slot filling model decisions. In this work, we propose a novel approach that: (i) learns to generate additional slot type specific features in order to improve accuracy and (ii) provides explanations for slot filling decisions for the first time in a joint NLU model. We perform an additional constrained supervision using a set of binary classifiers for the slot type specific feature learning, thus ensuring appropriate attention weights are learned in the process to explain slot filling decisions for utterances. Our model is inherently explainable and does not need any post-hoc processing. We evaluate our approach on two widely used datasets and show accuracy improvements. Moreover, a detailed analysis is also provided for the exclusive slot explainability.