  Response ranking in dialogues plays a crucial role in retrieval-based
conversational systems. In a multi-turn dialogue, to capture the gist of a
conversation, contextual information serves as essential knowledge to achieve
this goal. In this paper, we present a flexible neural framework that can
integrate contextual information from multiple channels. Specifically for the
current task, our approach is to provide two information channels in parallel,
Fusing Conversation history and domain knowledge extracted from Candidate
provenance (FCC), where candidate responses are curated, as contextual
information to improve the performance of multi-turn dialogue response ranking.
The proposed approach can be generalized as a module to incorporate
miscellaneous contextual features for other context-oriented tasks. We evaluate
our model on the MSDialog dataset widely used for evaluating conversational
response ranking tasks. Our experimental results show that our framework
significantly outperforms the previous state-of-the-art models, improving
Recall@1 by 7% and MAP by 4%. Furthermore, we conduct ablation studies to
evaluate the contributions of each information channel, and of the framework
components, to the overall ranking performance, providing additional insights
and directions for further improvements.
