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This paper proposes a unified computational framework, the Rational Interlocutor (RI) model, which treats language comprehension and production as mirror-image processes driven by a single rational computation over a partner's identity, fidelity, and knowledge. By formalizing how conversational partners are modeled to predict intent and optimize utterance design, the work advances fundamental machine learning theory in dialogue systems and natural language understanding. It offers testable predictions for how AI agents should adapt their communication strategies based on perceived partner competence.
A model of rational interlocutors: Unification of comprehension and production
arXiv:2609.32216v1 Announce Type: new Abstract: Who we communicate with influences both our interpretation of their utterances and the design of our own. Such adjustment to the conversational partner is studied as speaker modeling in comprehension and as audience design in production, with the two literatures having developed largely separately. We argue that both adjustments express one rational computation and propose the rational interlocutor (RI) model, a computational account unifying comprehension and production. An interlocutor maintains a model of their partner, defined by three parameters: an identity parameter {\Pi} sets the messages and forms expected from the partner; a fidelity parameter {\Phi} sets how reliably messages and utterances map onto each other for them; a knowledge parameter {\Lambda} sets how knowledgeable the partner is believed to be. Comprehension and production are thus mirror-image modes of one computation over the partner model. Comprehension chooses the message the partner most likely intends to convey, weighing how well each candidate fits the utterance against how likely this partner is to mean it. Production chooses the utterance from which the partner will best recover the message, weighed against the effort of saying it. This explains why comprehenders appear to rely less on the forms produced by a linguistically less competent speaker, while producers tend to invest more effort in designing forms for them. We conjecture that perceived linguistic competence decomposes into two of these quantities: fidelity and knowledge. Their contrasting profiles across second-language (L2) adults, children, and artificial partners produce distinct and testable predictions.
Abstract:Who we communicate with influences both our interpretation of their utterances and the design of our own. Such adjustment to the conversational partner is studied as speaker modeling in comprehension and as audience design in production, with the two literatures having developed largely separately. We argue that both adjustments express one rational computation and propose the rational interlocutor (RI) model, a computational account unifying comprehension and production. An interlocutor maintains a model of their partner, defined by three parameters: an identity parameter {\Pi} sets the messages and forms expected from the partner; a fidelity parameter {\Phi} sets how reliably messages and utterances map onto each other for them; a knowledge parameter {\Lambda} sets how knowledgeable the partner is believed to be. Comprehension and production are thus mirror-image modes of one computation over the partner model. Comprehension chooses the message the partner most likely intends to convey, weighing how well each candidate fits the utterance against how likely this partner is to mean it. Production chooses the utterance from which the partner will best recover the message, weighed against the effort of saying it. This explains why comprehenders appear to rely less on the forms produced by a linguistically less competent speaker, while producers tend to invest more effort in designing forms for them. We conjecture that perceived linguistic competence decomposes into two of these quantities: fidelity and knowledge. Their contrasting profiles across second-language (L2) adults, children, and artificial partners produce distinct and testable predictions.
A computational model of partner modeling in language comprehension and production. 62 pages, 5 figures, 3 tables, including supplementary materials
Computation and Language (cs.CL)
arXiv:2609.32216 [cs.CL]
(or arXiv:2609.32216v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.32216 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 359 words
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