Color Overmodification Emerges from Data-Driven Learning and Pragmatic Reasoning

• URL: http://arxiv.org/abs/2205.09172v1
• Date: Wed, 18 May 2022 18:42:43 GMT
• Title: Color Overmodification Emerges from Data-Driven Learning and Pragmatic Reasoning
• Authors: Fei Fang, Kunal Sinha, Noah D. Goodman, Christopher Potts, Elisa Kreiss
• Abstract summary: We show that speakers' referential expressions depart from communicative ideals in ways that help illuminate the nature of pragmatic language use. By adopting neural networks as learning agents, we show that overmodification is more likely with environmental features that are infrequent or salient.
• Score: 53.088796874029974
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Speakers' referential expressions often depart from communicative ideals in ways that help illuminate the nature of pragmatic language use. Patterns of overmodification, in which a speaker uses a modifier that is redundant given their communicative goal, have proven especially informative in this regard. It seems likely that these patterns are shaped by the environment a speaker is exposed to in complex ways. Unfortunately, systematically manipulating these factors during human language acquisition is impossible. In this paper, we propose to address this limitation by adopting neural networks (NN) as learning agents. By systematically varying the environments in which these agents are trained, while keeping the NN architecture constant, we show that overmodification is more likely with environmental features that are infrequent or salient. We show that these findings emerge naturally in the context of a probabilistic model of pragmatic communication.

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