  This paper presents a large-scale corpus for non-task-oriented dialogue
response selection, which contains over 27K distinct prompts more than 82K
responses collected from social media. To annotate this corpus, we define a
5-grade rating scheme: bad, mediocre, acceptable, good, and excellent,
according to the relevance, coherence, informativeness, interestingness, and
the potential to move a conversation forward. To test the validity and
usefulness of the produced corpus, we compare various unsupervised and
supervised models for response selection. Experimental results confirm that the
proposed corpus is helpful in training response selection models.
