Semantic Complexity in End-to-End Spoken Language Understanding

TL;DR

This paper investigates how the semantic complexity of datasets affects the performance of end-to-end spoken language understanding models, emphasizing the importance of dataset complexity in evaluating model capabilities.

Contribution

The study introduces empirical measures of semantic complexity and demonstrates their correlation with STI model performance across different datasets.

Findings

Performance improves as semantic complexity decreases.

Low complexity datasets yield near-perfect results.

Contextualizing performance with complexity measures reveals model applicability.

Abstract

End-to-end spoken language understanding (SLU) models are a class of model architectures that predict semantics directly from speech. Because of their input and output types, we refer to them as speech-to-interpretation (STI) models. Previous works have successfully applied STI models to targeted use cases, such as recognizing home automation commands, however no study has yet addressed how these models generalize to broader use cases. In this work, we analyze the relationship between the performance of STI models and the difficulty of the use case to which they are applied. We introduce empirical measures of dataset semantic complexity to quantify the difficulty of the SLU tasks. We show that near-perfect performance metrics for STI models reported in the literature were obtained with datasets that have low semantic complexity values. We perform experiments where we vary the semantic complexity of a large, proprietary dataset and show that STI model performance correlates with our semantic complexity measures, such that performance increases as complexity values decrease. Our results show that it is important to contextualize an STI model's performance with the complexity values of its training dataset to reveal the scope of its applicability.