Abstract
We present open domain response generation
with meta-words. A meta-word is a structured
record that describes various attributes of a response, and thus allows us to explicitly model
the one-to-many relationship within open domain dialogues and perform response generation in an explainable and controllable manner. To incorporate meta-words into generation, we enhance the sequence-to-sequence architecture with a goal tracking memory network that formalizes meta-word expression as
a goal and manages the generation process to
achieve the goal with a state memory panel and
a state controller. Experimental results on two
large-scale datasets indicate that our model
can significantly outperform several state-ofthe-art generation models in terms of response
relevance, response diversity, accuracy of oneto-many modeling, accuracy of meta-word expression, and human evaluation