Discourse relation identification has been an active area of research for
many years, and the challenge of identifying implicit relations remains largely
an unsolved task, especially in the context of an open-domain dialogue system.
Previous work primarily relies on a corpora of formal text which is inherently
non-dialogic, i.e., news and journals. This data however is not suitable to
handle the nuances of informal dialogue nor is it capable of navigating the
plethora of valid topics present in open-domain dialogue. In this paper, we
designed a novel discourse relation identification pipeline specifically tuned
for open-domain dialogue systems. We firstly propose a method to automatically
extract the implicit discourse relation argument pairs and labels from a
dataset of dialogic turns, resulting in a novel corpus of discourse relation
pairs; the first of its kind to attempt to identify the discourse relations
connecting the dialogic turns in open-domain discourse. Moreover, we have taken
the first steps to leverage the dialogue features unique to our task to further
improve the identification of such relations by performing feature ablation and
incorporating dialogue features to enhance the state-of-the-art model.

本论文旨在设计用于开放领域对话系统的新型话语关系识别流水线，该流水线可以自动提取隐式话语关系参数对和标签，并通过削减特征和整合对话特征等方法来提高辨识度。论文使用对话转换数据集得到了独特的话语关系对语料库，首次尝试确定开放领域对话中连接对话转换的话语关系。