@InProceedings{groves-tian-douratsos:2018:W18-65,
  author    = {Groves, Isabel  and  Tian, Ye  and  Douratsos, Ioannis},
  title     = {Treat the system like a human student: Automatic naturalness evaluation of generated text without reference texts},
  booktitle = {Proceedings of the 11th International Conference on Natural Language Generation},
  month     = {September},
  year      = {2018},
  address   = {Tilburg University, The Netherlands},
  publisher = {Association for Computational Linguistics},
  pages     = {109--118},
  abstract  = {The current most popular method for automatic Natural Language Generation (NLG) evaluation is comparing generated text with human-written reference sentences using a metrics system, which has drawbacks around reliability and scalability. We draw inspiration from second language (L2) assessment and extract a set of linguistic features to predict human judgments of sentence naturalness. Our experiment using a small dataset showed that the feature-based approach yields promising results, with the added potential of providing interpretability into the source of the problems.},
  url       = {http://www.aclweb.org/anthology/W18-6512}
}