Towards a Fully Interpretable and More Scalable RSA Model for Metaphor
  Understanding
Gaia Carenini Luca Bischetti Walter Schaeken Valentina Bambini
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 Large Language Models
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.