Explainable Slot Type Attentions to Improve Joint Intent Detection and Slot Filling
This work addresses the need for explainable natural language understanding systems, particularly for slot filling, which is incremental as it builds on existing joint models by adding explainability features.
The paper tackles the problem of improving accuracy and providing explanations for slot filling decisions in joint intent detection and slot filling models, showing accuracy improvements on two widely used datasets.
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.