2 research outputs found
Semi-Supervised Bootstrapping of Dialogue State Trackers for Task-Oriented Modelling
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.Comment: This article is published at EMNLP-IJCNLP 201