  Open domain response generation has achieved remarkable progress in recent
years, but sometimes yields short and uninformative responses. We propose a new
paradigm for response generation, that is response generation by editing, which
significantly increases the diversity and informativeness of the generation
results. Our assumption is that a plausible response can be generated by
slightly revising an existing response prototype. The prototype is retrieved
from a pre-defined index and provides a good start-point for generation because
it is grammatical and informative. We design a response editing model, where an
edit vector is formed by considering differences between a prototype context
and a current context, and then the edit vector is fed to a decoder to revise
the prototype response for the current context. Experiment results on a large
scale dataset demonstrate that the response editing model outperforms
generative and retrieval-based models on various aspects.
