  Controlling chatbot utterance generation with multiple attributes such as
personalities, emotions and dialogue acts is a practically useful but
under-studied problem. We propose a novel framework called DASC that possesses
strong controllability with a weighted decoding paradigm, while improving
generation quality with the grounding in an attribute semantics space.
Generation with multiple attributes is then intuitively implemented with an
interpolation of multiple attribute embeddings, which results in substantial
reduction in the model sizes. Experiments show that DASC can achieve high
control accuracy in generation task with the simultaneous control of 3 aspects
while also producing interesting and reasonably sensible responses, even in an
out-of-distribution robustness test.
