@inproceedings{utting-etal-2026-surprisal,
title = "Surprisal Minimisation over Goal-directed Alternatives Predicts Production Choice in Dialogue",
author = "Utting, Thomas P. and
Giulianelli, Mario and
Sinclair, Arabella",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1814/",
doi = "10.18653/v1/2026.acl-long.1814",
pages = "39090--39109",
ISBN = "979-8-89176-390-6",
abstract = "We model utterance production as probabilistic cost-sensitive choice over contextual alternatives, using information-theoretic notions of cost. We distinguish between goal-directed alternatives that realise a fixed communicative intent and goal-agnostic alternatives defined only by contextual plausibility, allowing us to derive speaker- and listener-oriented interpretations of different cost measures. We present a procedure to generate both types of alternative sets using language models. Analysing production choices in open-ended dialogue under both deterministic and noisy cost minimisation, we find that surprisal minimisation relative to goal-directed alternatives provides the strongest explanation of production choices. Uniformity-based costs show weaker overall predictive power, but their influence increases markedly when evaluated relative to goal-agnostic alternatives, consistent with listener-oriented accounts. More broadly, our study suggests that alternative-conditioned optimisation with LM-generated alternatives provides a principled framework for studying speaker and listener pressures in naturalistic language production."
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%0 Conference Proceedings
%T Surprisal Minimisation over Goal-directed Alternatives Predicts Production Choice in Dialogue
%A Utting, Thomas P.
%A Giulianelli, Mario
%A Sinclair, Arabella
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F utting-etal-2026-surprisal
%X We model utterance production as probabilistic cost-sensitive choice over contextual alternatives, using information-theoretic notions of cost. We distinguish between goal-directed alternatives that realise a fixed communicative intent and goal-agnostic alternatives defined only by contextual plausibility, allowing us to derive speaker- and listener-oriented interpretations of different cost measures. We present a procedure to generate both types of alternative sets using language models. Analysing production choices in open-ended dialogue under both deterministic and noisy cost minimisation, we find that surprisal minimisation relative to goal-directed alternatives provides the strongest explanation of production choices. Uniformity-based costs show weaker overall predictive power, but their influence increases markedly when evaluated relative to goal-agnostic alternatives, consistent with listener-oriented accounts. More broadly, our study suggests that alternative-conditioned optimisation with LM-generated alternatives provides a principled framework for studying speaker and listener pressures in naturalistic language production.
%R 10.18653/v1/2026.acl-long.1814
%U https://aclanthology.org/2026.acl-long.1814/
%U https://doi.org/10.18653/v1/2026.acl-long.1814
%P 39090-39109
Markdown (Informal)
[Surprisal Minimisation over Goal-directed Alternatives Predicts Production Choice in Dialogue](https://aclanthology.org/2026.acl-long.1814/) (Utting et al., ACL 2026)
ACL