  End-to-end conversational recommendation systems (CRS) generate responses by
leveraging both dialog history and a knowledge base (KB). A CRS mainly faces
three key challenges: (1) at each turn, it must decide if recommending a KB
entity is appropriate; if so, it must identify the most relevant KB entity to
recommend; and finally, it must recommend the entity in a fluent utterance that
is consistent with the conversation history. Recent CRSs do not pay sufficient
attention to these desiderata, often generating unfluent responses or not
recommending (relevant) entities at the right turn. We introduce a new CRS we
call CoRE-CoG. CoRE-CoG addresses the limitations in prior systems by
implementing (1) a recommendation trigger that decides if the system utterance
should include an entity, (2) a type pruning module that improves the relevance
of recommended entities, and (3) a novel constrained response generator to make
recommendations while maintaining fluency. Together, these modules ensure
simultaneous accurate recommendation decisions and fluent system utterances.
Experiments with recent benchmarks show the superiority particularly on
conditional generation sub-tasks with close to 10 F1 and 4 Recall@1 percent
points gain over baselines.
