  Dialogue engines that incorporate different types of agents to converse with
humans are popular.
  However, conversations are dynamic in the sense that a selected response will
change the conversation on-the-fly, influencing the subsequent utterances in
the conversation, which makes the response selection a challenging problem.
  We model the problem of selecting the best response from a set of responses
generated by a heterogeneous set of dialogue agents by taking into account the
conversational history, and propose a \emph{Neural Response Selection} method.
  The proposed method is trained to predict a coherent set of responses within
a single conversation, considering its own predictions via a curriculum
training mechanism.
  Our experimental results show that the proposed method can accurately select
the most appropriate responses, thereby significantly improving the user
experience in dialogue systems.
