CET2: Modelling Topic Transitions for Coherent and Engaging
  Knowledge-Grounded Conversations
Lin Xu Qixian Zhou Jinlan Fu See-Kiong Ng
Knowledge-grounded dialogue systems aim to generate coherent and engaging
responses based on the dialogue contexts and selected external knowledge.
Previous knowledge selection methods tend to rely too heavily on the dialogue
contexts or over-emphasize the new information in the selected knowledge,
resulting in the selection of repetitious or incongruous knowledge and further
generating repetitive or incoherent responses, as the generation of the
response depends on the chosen knowledge. To address these shortcomings, we
introduce a Coherent and Engaging Topic Transition (CET2) framework to model
topic transitions for selecting knowledge that is coherent to the context of
the conversations while providing adequate knowledge diversity for topic
development. Our CET2 framework considers multiple factors for knowledge
selection, including valid transition logic from dialogue contexts to the
following topics and systematic comparisons between available knowledge
candidates. Extensive experiments on two public benchmarks demonstrate the
superiority and the better generalization ability of CET2 on knowledge
selection. This is due to our well-designed transition features and comparative
knowledge selection strategy, which are more transferable to conversations
about unseen topics. Analysis of fine-grained knowledge selection accuracy also
shows that CET2 can better balance topic entailment (contextual coherence) and
development (knowledge diversity) in dialogue than existing approaches.