  Conversational agents are gaining popularity with the increasing ubiquity of
smart devices. However, training agents in a data driven manner is challenging
due to a lack of suitable corpora. This paper presents a novel method for
gathering topical, unstructured conversational data in an efficient way:
self-dialogues through crowd-sourcing. Alongside this paper, we include a
corpus of 3.6 million words across 23 topics. We argue the utility of the
corpus by comparing self-dialogues with standard two-party conversations as
well as data from other corpora.
