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.

研究了基于编码器 - 解码器模型的开放领域对话系统的自动回复方法，并提出一种将内容单词序列作为中间表示的方法，并通过将学习和评估的重点从完整句子转移到内容单词序列，提高了生成语句相关性的方法。