Bayesian Approaches to Preference-based Answer Generation in Dialogue

该项目建立在DFG资助的项目“销售对话中回答生成的实用要求”的结果之上。我们打算调查使用贝叶斯概率方法生成的语用适当的答案在对话系统。我们想研究三种不同的语言手段，显示出对对话者的（可能是相反的）偏好的敏感性：数字和等级表达，饼干条件（关联条件），和评价状语。这些手段都能够触发基于假设偏好的含义。这些现象要求对话系统在其信息状态中管理偏好和语用推断意义。这反过来又需要概率推理，因为偏好并不总是以高度的确定性知道。数据采集和正式建模将在方法上交织在一起。对话系统的各个组成部分的评估将通过比较它们与语义导向的基础系统表现出没有语用能力。

This project builds on the results of the DFG-funded project 'Pragmatic Requirements for Answer Generation in a Sales Dialogue'. We intend to investigate the use of Bayesian probabilistic approaches to the generation of pragmatically appropriate answers in a dialogue system. We want to study three different linguistic means that show a sensitivity to interlocutors' (possibly opposing) preferences: numerical and gradable expressions, biscuit conditionals (relevance conditionals), and evaluative adverbials. Each of these means are able to trigger implicatures based on supposed preferences. These phenomena require the dialogue system to manage preferences and pragmatically inferred meaning in its information states. This in turn requires probabilistic reasoning, given that preferences are not always known with high certainty. Data acquisition and formal modeling will be methodologically intertwined. The evaluation of the respective components of the dialogue system will be performed by comparing them with a semantically oriented base system exhibiting no pragmatic competence.