@inproceedings{forrest-etal-2018-towards,
    title = "Towards making {NLG} a voice for interpretable Machine Learning",
    author = "Forrest, James  and
      Sripada, Somayajulu  and
      Pang, Wei  and
      Coghill, George",
    editor = "Krahmer, Emiel  and
      Gatt, Albert  and
      Goudbeek, Martijn",
    booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
    month = nov,
    year = "2018",
    address = "Tilburg University, The Netherlands",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest_wac_2008/W18-6522/",
    doi = "10.18653/v1/W18-6522",
    pages = "177--182",
    abstract = "This paper presents a study to understand the issues related to using NLG to humanise explanations from a popular interpretable machine learning framework called LIME. Our study shows that self-reported rating of NLG explanation was higher than that for a non-NLG explanation. However, when tested for comprehension, the results were not as clear-cut showing the need for performing more studies to uncover the factors responsible for high-quality NLG explanations."
}