Why Are Parsing Actions for Understanding Message Hierarchies Not Random?

TL;DR

This paper investigates why human parsing strategies for understanding message hierarchies are non-random by testing models with complex inputs and surprisal-based objectives, revealing the importance of structured parsing for effective communication.

Contribution

It introduces two modifications—more complex hierarchical inputs and surprisal-based objectives—to assess the necessity of non-random parsing strategies in communication models.

Findings

Random parsing strategies struggle with complex hierarchical inputs.

Surprisal-based objectives promote more structured and effective parsing.

Structured parsing improves communication accuracy in hierarchical message understanding.

Abstract

If humans understood language by randomly selecting parsing actions, it might have been necessary to construct a robust symbolic system capable of being interpreted under any hierarchical structure. However, human parsing strategies do not seem to follow such a random pattern. Why is that the case? In fact, a previous study on emergent communication using models with hierarchical biases have reported that agents adopting random parsing strategies$\unicode{x2013}$ones that deviate significantly from human language comprehension$\unicode{x2013}$can achieve high communication accuracy. In this study, we investigate this issue by making two simple and natural modifications to the experimental setup: (I) we use more complex inputs that have hierarchical structures, such that random parsing makes semantic interpretation more difficult, and (II) we incorporate a surprisal-related term, which is known to influence the order of words and characters in natural language, into the objective function. With these changes, we evaluate whether agents employing random parsing strategies still maintain high communication accuracy.