  Various encoder-decoder models have been applied to response generation in
open-domain dialogs, but a majority of conventional models directly learn a
mapping from lexical input to lexical output without explicitly modeling
intermediate representations. Utilizing language hierarchy and modeling
intermediate information have been shown to benefit many language understanding
and generation tasks. Motivated by Broca's aphasia, we propose to use a content
word sequence as an intermediate representation for open-domain response
generation. Experimental results show that the proposed method improves content
relatedness of produced responses, and our models can often choose correct
grammar for generated content words. Meanwhile, instead of evaluating complete
sentences, we propose to compute conventional metrics on content word
sequences, which is a better indicator of content relevance.
