Towards a Fully Interpretable and More Scalable RSA Model for Metaphor Understanding (2404.02983v1)

Abstract: The Rational Speech Act (RSA) model provides a flexible framework to model pragmatic reasoning in computational terms. However, state-of-the-art RSA models are still fairly distant from modern machine learning techniques and present a number of limitations related to their interpretability and scalability. Here, we introduce a new RSA framework for metaphor understanding that addresses these limitations by providing an explicit formula - based on the mutually shared information between the speaker and the listener - for the estimation of the communicative goal and by learning the rationality parameter using gradient-based methods. The model was tested against 24 metaphors, not limited to the conventional $\textit{John-is-a-shark}$ type. Results suggest an overall strong positive correlation between the distributions generated by the model and the interpretations obtained from the human behavioral data, which increased when the intended meaning capitalized on properties that were inherent to the vehicle concept. Overall, findings suggest that metaphor processing is well captured by a typicality-based Bayesian model, even when more scalable and interpretable, opening up possible applications to other pragmatic phenomena and novel uses for increasing LLMs interpretability. Yet, results highlight that the more creative nuances of metaphorical meaning, not strictly encoded in the lexical concepts, are a challenging aspect for machines.

• The paper introduces a novel RSA model that integrates gradient-based optimization to estimate communicative goals in metaphor comprehension.
• It employs an explicit probability formula and behavioral experiments to assess scalability and interpretability across varied metaphorical expressions.
• Results show strong alignment with human interpretation while underscoring challenges in capturing the creative nuances of metaphors.

Enhancing Metaphor Understanding through an Interpretable RSA Model

Introduction to the RSA Framework and Metaphor Understanding

The Rational Speech Act (RSA) model operates on the interface between game-theoretic and decision-theoretic principles to explicate verbal communication. It frames language use as a probabilistic signaling game, significantly informing the comprehension of a myriad of pragmatic phenomena, ranging from reference determination to the understanding of non-literal language, such as metaphors. Despite its computational and mathematical foundation, the RSA framework's integration with cutting-edge machine learning techniques remains superficial. RSA models grapple with high computational demands, scalability issues, and a notable dearth in interpretability particularly when out-of-domain generalization is attempted.

Objective and Novelty of the Proposed RSA Model

In this paper, we aim to bridge the gap between the RSA framework and contemporary AI methodologies through a novel RSA model specifically tailored for metaphor comprehension. This model introduces significant advancements by incorporating an explicit formula to estimate communicative goals, utilizing gradient-based methods to learn rationality parameters, and testing against a diversified set of metaphors beyond the traditional "John-is-a-shark" type.

Key Features of the Computational Model

Our model maintains the core structure of its predecessors but innovates in several critical aspects:

• Explicit Estimation of Communicative Goal Probability: The model incorporates a formula to explicitly estimate the probability distribution of communicative goals based on the minimal conversational context and mutual information shared between the speaker and listener.
• Gradient-Based Learning of Rationality Parameter: Unlike prior models that relied on interpolation for parameter estimation, our approach leverages gradient-based optimization techniques to efficiently learn the rationality parameter ($\lambda$), enhancing the model's scalability and interpretability.
• Comprehensive Testing on Diverse Metaphors: Our analysis extends to metaphors involving a variety of topics and vehicles, examining the model's generalizability and effectiveness across a broader spectrum of metaphorical expressions.

Evaluation and Findings

The novel RSA model was evaluated through behavioral experiments targeting metaphor understanding. The results showcased a strong positive correlation between the model-generated distributions and human interpretations, particularly when the metaphors employed vehicle-inherent properties. However, the model exhibited limitations in grasping the nuanced, creative aspects of metaphor not directly encoded in lexical concepts. This discrepancy underscores the challenges machines face in apprehending the most inventive facets of human language.

Implications and Future Directions

The proposed RSA model demonstrates considerable promise in enhancing the interpretability and scalability of computational models for metaphor understanding. By effectively integrating gradient-based learning techniques, the model not only aligns more closely with state-of-the-art AI but also suggests a pathway for improving LLMs' (LLMs) interpretability. Our findings also pave the way for applying this refined RSA framework to a broader array of pragmatic phenomena beyond metaphor, potentially offering a deeper understanding of complex linguistic and cognitive processes.

It is essential to acknowledge, however, that the journey to fully comprehend the intricacies of metaphorical language through computational models is ongoing. The evident gap in capturing creative and context-dependent metaphorical nuances invites further research, perhaps focusing on integrating sensory experience, emotive resonance, and the context's dynamic aspects into the RSA framework.

Future endeavors could also explore the model's applicability in deciphering the operational principles of the latest generation of LLMs or developing novel algorithms to tackle the creative and less predictable segments of metaphor interpretation. As we continue to refine and expand upon RSA-based models, the convergence of classic pragmatic theory and modern machine learning techniques holds untold potential for advancing our understanding of human language and cognition.