  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.
