发表机构
The Chinese University of Hong Kong（香港中文大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出理性对话者模型，用三个参数统一理解与生成中的伙伴适应，解释语言能力差异下的行为，并给出可检验预测。
AI 中文摘要
我们与谁交流，既影响我们对他们话语的理解，也影响我们自身话语的设计。这种对对话伙伴的适应，在理解研究中被称为说话者建模，在生成研究中被称为受众设计，而这两方面的文献在很大程度上是分别发展的。我们认为，这两种适应都体现了一种理性的计算，并提出了理性对话者（RI）模型，这是一个统一理解与生成的计算框架。对话者维护一个关于其伙伴的模型，该模型由三个参数定义：一个身份参数Π，设定伙伴预期传达的消息和形式；一个保真度参数Φ，设定消息和话语在伙伴那里相互映射的可靠性；一个知识参数Λ，设定伙伴被认为有多博学。因此，理解与生成是对伙伴模型进行同一种计算的镜像模式。理解选择伙伴最可能意图传达的消息，权衡每个候选消息与话语的匹配程度以及该伙伴表达此意的可能性。生成则选择伙伴最能从中恢复消息的话语，并权衡说出该话语的努力。这解释了为什么理解者似乎较少依赖语言能力较弱的说话者所产生的形式，而生成者则倾向于投入更多努力为这类伙伴设计形式。我们推测，感知到的语言能力可分解为这两个量：保真度和知识。它们在第二语言（L2）成人、儿童和人工伙伴中的不同特征，产生了独特且可检验的预测。
英文摘要
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 Π sets the messages and forms expected from the partner; a fidelity parameter Φ sets how reliably messages and utterances map onto each other for them; a knowledge parameter Λ 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.
CommentsA computational model of partner modeling in language comprehension and production. 62 pages, 5 figures, 3 tables, including supplementary materials
