Modelling the Information Density of Event Sequences in Texts (A03)

项目A3旨在从数据中收集关于原型事件序列的形式化知识-脚本知识-并使用它来改进自然语言处理算法以及我们对人类交流中语言编码选择和解释的理解。该项目将开发学习脚本的方法，从未注释的文本中广泛覆盖，并扩展脚本事件的表征，提供有关其先决条件和影响的信息，以跟踪事件之间的因果关系。这些更深入和更广泛的覆盖脚本模型将被应用于各种自然语言处理任务，并用于建模语用解释;我们将使用和扩展的理性言语行为（RSA）模型作为建模语用推理的框架，并探讨如何RSA模型可以与现有的概念中使用的SFB，特别是UID假设。

Project A3 aims at collecting formalized knowledge about prototypical sequences of events – script knowledge – from data, and using it to improve algorithms for natural language processing and our understanding of linguistic encoding choice and interpretation in human communication. The project will develop methods for learning scripts with wide coverage from unannotated texts and extend the representations of script events with information about their preconditions and effects to keep track of causal connections between events. These deeper and wider-coverage script models will be applied to various natural language processing tasks, and used to model pragmatic interpretations; we will use and extend the Rational Speech Act (RSA) model as a framework for modelling pragmatic inferences and explore how the RSA model can be related to existing notions used in the SFB, specifically the UID hypothesis.