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

The Whole Truth and Nothing But the Truth: Faithful and Controllable  Dialogue Response Generation with Dataflow Transduction and Constrained  Decoding