Dialogue systems benefit greatly from optimizing on detailed annotations,
such as transcribed utterances, internal dialogue state representations and
dialogue act labels. However, collecting these annotations is expensive and
time-consuming, holding back development in the area of dialogue modelling. In
this paper, we investigate semi-supervised learning methods that are able to
reduce the amount of required intermediate labelling. We find that by
leveraging un-annotated data instead, the amount of turn-level annotations of
dialogue state can be significantly reduced when building a neural dialogue
system. Our analysis on the MultiWOZ corpus, covering a range of domains and
topics, finds that annotations can be reduced by up to 30\% while maintaining
equivalent system performance. We also describe and evaluate the first
end-to-end dialogue model created for the MultiWOZ corpus.

本文研究了利用半监督学习方法在构建神经对话系统时可以减少中间标签的数量，发现可以利用未注释数据来显著减少对话状态的转换级别注释，同时在 MultiWOZ 语料库上进行了分析并提出了第一个端到端的对话模型。