  In a real-world dialogue system, generated text must be truthful and
informative while remaining fluent and adhering to a prescribed style.
Satisfying these constraints simultaneously is difficult for the two
predominant paradigms in language generation: neural language modeling and
rule-based generation. We describe a hybrid architecture for dialogue response
generation that combines the strengths of both paradigms. The first component
of this architecture is a rule-based content selection model defined using a
new formal framework called dataflow transduction, which uses declarative rules
to transduce a dialogue agent's actions and their results (represented as
dataflow graphs) into context-free grammars representing the space of
contextually acceptable responses. The second component is a constrained
decoding procedure that uses these grammars to constrain the output of a neural
language model, which selects fluent utterances. Our experiments show that this
system outperforms both rule-based and learned approaches in human evaluations
of fluency, relevance, and truthfulness.
