Don't Say That! Making Inconsistent Dialogue Unlikely with Unlikelihood Training

@article{Li2020DontST,
  title={Don't Say That! Making Inconsistent Dialogue Unlikely with Unlikelihood Training},
  author={Margaret Li and Stephen Roller and Ilia Kulikov and S. Welleck and Y-Lan Boureau and Kyunghyun Cho and J. Weston},
  journal={ArXiv},
  year={2020},
  volume={abs/1911.03860}
}
Generative dialogue models currently suffer from a number of problems which standard maximum likelihood training does not address. They tend to produce generations that (i) rely too much on copying from the context, (ii) contain repetitions within utterances, (iii) overuse frequent words, and (iv) at a deeper level, contain logical flaws. In this work we show how all of these problems can be addressed by extending the recently introduced unlikelihood loss (Welleck et al., 2019) to these cases… Expand
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