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  A simple repair mechanism can alleviate computational demands of pragmatic reasoning: simulations and complexity analysis

Van Arkel, J., Woensdregt, M., Dingemanse, M., & Blokpoel, M. (2020). A simple repair mechanism can alleviate computational demands of pragmatic reasoning: simulations and complexity analysis. In R. Fernández, & T. Linzen (Eds.), Proceedings of the 24th Conference on Computational Natural Language Learning (CoNLL 2020) (pp. 177-194). Stroudsburg, PA, USA: The Association for Computational Linguistics.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0007-4BDC-E Version Permalink: https://hdl.handle.net/21.11116/0000-0007-64B8-9
Genre: Conference Paper

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https://hdl.handle.net/21.11116/0000-0007-64B9-8
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2020
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 Creators:
Van Arkel, Jacqueline, Author
Woensdregt, Marieke1, Author
Dingemanse, Mark1, 2, Author           
Blokpoel, Mark, Author
Affiliations:
1Center for Language Studies, External Organizations, ou_55238              
2Multimodal Language and Cognition, Radboud University Nijmegen, External Organizations, ou_3055480              

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 Abstract: How can people communicate successfully while keeping resource costs low in the face of ambiguity? We present a principled theoretical analysis comparing two strategies for disambiguation in communication: (i) pragmatic reasoning, where communicators reason about each other, and (ii) other-initiated repair, where communicators signal and resolve trouble interactively. Using agent-based simulations and computational complexity analyses, we compare the efficiency of these strategies in terms of communicative success, computation cost and interaction cost. We show that agents with a simple repair mechanism can increase efficiency, compared to pragmatic agents, by reducing their computational burden at the cost of longer interactions. We also find that efficiency is highly contingent on the mechanism, highlighting the importance of explicit formalisation and computational rigour.

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Language(s): eng - English
 Dates: 2020-10-20
 Publication Status: Published online
 Pages: -
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 Table of Contents: -
 Rev. Type: Peer
 Identifiers: -
 Degree: -

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Title: the 24th (Virtual) Conference on Computational Natural Language Learning (CoNLL 2020)
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Start-/End Date: 2020-11-19 - 2020-11-20

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Title: Proceedings of the 24th Conference on Computational Natural Language Learning (CoNLL 2020)
Source Genre: Proceedings
 Creator(s):
Fernández, Raquel, Editor
Linzen, Tal, Editor
Affiliations:
-
Publ. Info: Stroudsburg, PA, USA : The Association for Computational Linguistics
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 177 - 194 Identifier: -