@InProceedings{holtzman-EtAl:2018:Long,
  author    = {Holtzman, Ari  and  Buys, Jan  and  Forbes, Maxwell  and  Bosselut, Antoine  and  Golub, David  and  Choi, Yejin},
  title     = {Learning to Write with Cooperative Discriminators},
  booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2018},
  address   = {Melbourne, Australia},
  publisher = {Association for Computational Linguistics},
  pages     = {1638--1649},
  abstract  = {Despite their local fluency, long-form text generated from RNNs is often generic, repetitive, and even self-contradictory. We propose a unified learning framework that collectively addresses all the above issues by composing a committee of discriminators that can guide a base RNN generator towards more globally coherent generations. More concretely, discriminators each specialize in a different principle of communication, such as Grice's maxims, and are collectively combined with the base RNN generator through a composite decoding objective. Human evaluation demonstrates that text generated by our model is preferred over that of baselines by a large margin, significantly enhancing the overall coherence, style, and information of the generations.},
  url       = {http://www.aclweb.org/anthology/P18-1152}
}