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The Whole Truth and Nothing But the Truth: Faithful and Controllable Dialogue Response Generation with Dataflow Transduction and Constrained Decoding. (arXiv:2209.07800v1 [cs.CL])
Sept. 19, 2022, 1:15 a.m. | Hao Fang, Anusha Balakrishnan, Harsh Jhamtani, John Bufe, Jean Crawford, Jayant Krishnamurthy, Adam Pauls, Jason Eisner, Jacob Andreas, Dan Klein
cs.CL updates on arXiv.org arxiv.org
In a real-world dialogue system, generated responses must satisfy several
interlocking constraints: being informative, truthful, and easy to control. The
two predominant paradigms in language generation -- neural language modeling
and rule-based generation -- both struggle to satisfy these constraints. Even
the best neural models are prone to hallucination and omission of information,
while existing formalisms for rule-based generation make it difficult to write
grammars that are both flexible and fluent. We describe a hybrid architecture
for dialogue response generation …
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