本文介绍了一种基于自然语言理解(NLU)的议论文对话系统框架。我们的方法可以区分多个用户意图，并识别用户在其自然语言中引用的系统参数。我们的模型适用于一个议论性对话系统，该系统允许用户告知自己关于一个有争议的话题并建立自己的观点。为了评估所提出的方法，我们收集了用户与各自系统交互的话语，并在一个广泛的在线研究中标注意图和参考论点。数据收集包括多个主题和两种不同的用户类型(来自英国的母语使用者和来自中国的非母语使用者)。评价表明所使用的技术比基线方法有明显的优势，以及针对新主题和不同的语言能力以及用户的文化背景所提出的方法的稳健性。
原文标题：Natural Language Understanding for Argumentative Dialogue Systems in the Opinion Building Domain
原文：This paper introduces a natural language understanding (NLU) framework for argumentative dialogue systems in the information-seeking and opinion building domain. Our approach distinguishes multiple user intents and identifies system arguments the user refers to in his or her natural language utterances. Our model is applicable in an argumentative dialogue system that allows the user to inform him-/herself about and build his/her opinion towards a controversial topic. In order to evaluate the proposed approach, we collect user utterances for the interaction with the respective system and labeled with intent and reference argument in an extensive online study. The data collection includes multiple topics and two different user types (native speakers from the UK and non-native speakers from China). The evaluation indicates a clear advantage of the utilized techniques over baseline approaches, as well as a robustness of the proposed approach against new topics and different language proficiency as well as cultural background of the user.
Natural Language Understanding for Argumentative Dialogue Systems in the Opinion Building Domain.pdf