Referring Expressions with Rational Speech Act Framework: A Probabilistic Approach

TL;DR

This paper integrates the Rational Speech Act framework with deep learning to improve referring expression generation in complex visual scenes, emphasizing explainability and human comprehension over pure accuracy.

Contribution

It introduces a probabilistic RSA-based method for REG on large datasets, combining Bayesian pragmatics with deep learning, and analyzes its strengths and limitations.

Findings

Outperforms similar RSA approaches in human comprehension.

Less accurate than state-of-the-art deep learning models.

Excels under limited data scenarios.

Abstract

This paper focuses on a referring expression generation (REG) task in which the aim is to pick out an object in a complex visual scene. One common theoretical approach to this problem is to model the task as a two-agent cooperative scheme in which a `speaker' agent would generate the expression that best describes a targeted area and a `listener' agent would identify the target. Several recent REG systems have used deep learning approaches to represent the speaker/listener agents. The Rational Speech Act framework (RSA), a Bayesian approach to pragmatics that can predict human linguistic behavior quite accurately, has been shown to generate high quality and explainable expressions on toy datasets involving simple visual scenes. Its application to large scale problems, however, remains largely unexplored. This paper applies a combination of the probabilistic RSA framework and deep learning approaches to larger datasets involving complex visual scenes in a multi-step process with the aim of generating better-explained expressions. We carry out experiments on the RefCOCO and RefCOCO+ datasets and compare our approach with other end-to-end deep learning approaches as well as a variation of RSA to highlight our key contribution. Experimental results show that while achieving lower accuracy than SOTA deep learning methods, our approach outperforms similar RSA approach in human comprehension and has an advantage over end-to-end deep learning under limited data scenario. Lastly, we provide a detailed analysis on the expression generation process with concrete examples, thus providing a systematic view on error types and deficiencies in the generation process and identifying possible areas for future improvements.