  Many open-domain dialogue systems rely on multiple response generators, any
of which can contribute a response to the dialogue in a particular context.
Thus the ability to compare potential responses and then select the best plays
an important role in ensuring a dialogue system is coherent and engaging.
Dialogue coherence goes beyond simply remaining on topic -- some trivia may be
on topic and engaging when mentioned out of the blue, but may not be coherent
and grounded in the context of the conversation. We carry out experiments on
response selection in the Athena system, an Alexa Prize SocialBot that has
dedicated content and multiple topic-specific response generators for a large
number of topics. First, we collect a corpus of Athena conversations with live
human traffic, where potential responses from all enabled response generators
are logged and subsequently annotated for response quality. We compare several
off-the-shelf response ranking methods for open-domain dialogue to
Athena-Heuristic, a heuristic response ranker that was field-tested in Athena
during the third Alexa Prize competition. We also compare these to a
transformer-based response ranker we call Athena-RR, that we train on our
Athena conversations. Athena-RR uses both the conversational context and the
dialogue state to rank the potential responses. We find that Athena-RR with a
Recall@1 of 70.79\% outperforms Athena-Heuristic and all of the off-the-shelf
rankers by a large margin. We then conduct a live A/B study comparing
Athena-Heuristic to Athena-RR in a 6,358 conversations with Alexa users. We
show that Athena-RR leads to significantly longer conversations that receive
significantly higher user ratings than the heuristic rule-based ranker.
