@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://aclanthology.org/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.",
}
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%0 Conference Proceedings
%T Towards making NLG a voice for interpretable Machine Learning
%A Forrest, James
%A Sripada, Somayajulu
%A Pang, Wei
%A Coghill, George
%Y Krahmer, Emiel
%Y Gatt, Albert
%Y Goudbeek, Martijn
%S Proceedings of the 11th International Conference on Natural Language Generation
%D 2018
%8 November
%I Association for Computational Linguistics
%C Tilburg University, The Netherlands
%F forrest-etal-2018-towards
%X 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.
%R 10.18653/v1/W18-6522
%U https://aclanthology.org/W18-6522
%U https://doi.org/10.18653/v1/W18-6522
%P 177-182
Markdown (Informal)
[Towards making NLG a voice for interpretable Machine Learning](https://aclanthology.org/W18-6522) (Forrest et al., INLG 2018)
ACL
- James Forrest, Somayajulu Sripada, Wei Pang, and George Coghill. 2018. Towards making NLG a voice for interpretable Machine Learning. In Proceedings of the 11th International Conference on Natural Language Generation, pages 177–182, Tilburg University, The Netherlands. Association for Computational Linguistics.