借助（语言）朋友之力：多方随意对话的主题分割
With a Little Help from my (Linguistic) Friends: Topic Segmentation of Multi-party Casual Conversations
AI总结：
本文采用形式化方法对多方随意对话进行主题分割，识别有意义特征以理解对话主题结构，达到与机器学习模型相当的准确率。
摘要：
主题在对话的全局组织中扮演着重要角色，因为当前讨论的内容限制了参与者可能的贡献。理解主题在互动中的组织方式，将有助于我们洞察对话结构，而不仅仅是话语序列。然而，研究这种高层结构是一项复杂的任务，我们尝试通过先将对话分割成较小的、主题连贯的话语集合来接近这一目标。理解这些片段之间的互动，将使我们能够在对话层面提出一个主题组织模型。在本文中，我们处理开放领域对话，并试图以形式化方法达到与近期基于机器学习主题分割模型相当的水平。我们识别出的对该任务有意义的特征，有助于我们更好地理解对话的主题结构。
英文摘要：
Topics play an important role in the global organisation of a conversation as what is currently discussed constrains the possible contributions of the participant. Understanding the way topics are organised in interaction would provide insight on the structure of dialogue beyond the sequence of utterances. However, studying this high-level structure is a complex task that we try to approach by first segmenting dialogues into smaller topically coherent sets of utterances. Understanding the interactions between these segments would then enable us to propose a model of topic organisation at a dialogue level. In this paper we work with open-domain conversations and try to reach a comparable level of accuracy as recent machine learning based topic segmentation models but with a formal approach. The features we identify as meaningful for this task help us understand better the topical structure of a conversation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
