Probabilistic Modelling of pragmatic processing and utterance production

尽管存在巨大的不确定性，但说话者和听者往往能迅速有效地沟通。每个对话者都可能不确定对方想要什么、相信什么或认为什么重要。概率语用学是解决不确定互动问题的一种正式方法，它建立在认识论逻辑和博弈论的见解基础上，但它足够灵活，可以适应概率认知模型中关于自然认知机制、偏见和对话者可能存在的资源限制的假设。越来越多的研究人员投入到概率语用学中，表明概念问题可以在这个框架中解决，并且它允许与实验数据惊人地吻合，包括经验现实的软、模糊和梯度方面。我们在这个优先项目第一阶段的工作有助于将概率语用学建立为研究形式语言理论和实验数据之间联系的重要工具。特别地，我们研究了指称表达和模糊量化表达的使用和解释，主要集中在/some/， /many/和/few/上。该项目的第二阶段将坚持这些调查领域，以解决迄今为止尚未得到足够重视的概率语用学的两个一般问题：(i)处理和（ii）自然，自由的话语产生。最重要的是，虽然概率语用学已经成功地应用于心理语言学实验中的选择行为建模（即语用思考或加工的结果），但该框架中的任何模型都没有接近与增量加工相关的数据。因此，该项目的第二阶段提出了一种基于预测、增量和（近似）理想解释的方案，对视觉世界中自定节奏阅读任务和眼动追踪数据进行概率建模的方法。概率语用学模型的另一个紧迫问题是可扩展性。因此，在本项目的第二阶段，我们将研究对话者可能希望沟通的更复杂的情况。我们也会操纵对话者为什么或出于什么目的想要谈论某种情况。特别是，我们建议扩展概率模型以涵盖议论性话语的情况，在这种情况下，说话者发表的话语是为了说服听者接受特定的目标命题。在这一切中，我们将从（半）自由的生产任务中收集数据，并寻求提供能够解释这些数据的丰富和可能的开放性的话语生产模型。

Speakers and hearers often manage to communicate swiftly and efficiently despite tremendous uncertainty. Each interlocutor may be uncertain about what the other wants, believes or considers important. Probabilistic pragmatics is a formal approach to this problem of uncertain interaction, which builds on insights from epistemic logic and game theory but is flexible enough to accommodate in probabilistic cognitive models assumptions about natural cognitive mechanisms, biases and resource limitations that conversationalists may have. A dedicated and growing number of researchers have invested in probabilistic pragmatics, showing that conceptual problems can be solved in this framework and that it allows for an astonishingly good fit to experimental data, including soft, fuzzy and gradient aspects of empirical reality.Our work in the first phase of this priority program has contributed to establishing probabilistic pragmatics as a serious tool for investigating the connection between formal linguistic theory and experimental data. In particular, we have investigated the use and interpretation of referential expressions and vague quantifying expressions, mostly focusing on /some/, /many/ and /few/. The second phase of this project would stick with these domains of inquiry in order to address two general issues with probabilistic pragmatics that have not received sufficient attention so far: (i) processing and (ii) natural, free utterance production.Most importantly, while probabilistic pragmatics has been successfully applied to the modeling of choice behavior in psycholinguistic experiments (i.e., to the outcome of pragmatic deliberation or processing), no model from this framework has approached data related to incremental processing. The second phase of this project therefore proposes an approach to probabilistic modeling of data from self-paced reading tasks and eye-tracking in visual worlds, based on a scheme of predictive, incremental and (approximately) ideal interpretation.Another pressing issue with probabilistic pragmatics models concerns their scalability. In the second phase of this project, we will therefore look at more complex situations which interlocutors might wish to communicate about. We also manipulate why or for what purpose interlocutors may wish to talk about a situation. In particular, we propose to extend probabilistic modeling to cover cases of argumentative discourse, where the speaker makes an utterance that is to persuade the listener of a particular goal proposition. In all this, we will gather data from (semi-)free production tasks and seek to supply models of utterance production that can account for the rich and possibly open-ended nature of such data.