Towards making NLG a voice for interpretable Machine Learning

James Forrest; Somayajulu Sripada; Wei Pang; George Coghill

Towards making NLG a voice for interpretable Machine Learning

James Forrest, Somayajulu Sripada, Wei Pang, George Coghill

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper presents a study to understand the issues related to using NLG to humanise explanations from a popular interpretable machine learning frameworkcalled LIME. Our study shows that selfreported rating of NLG explanation washigher than that for a non-NLG explanation. However, when tested for comprehension, the results were not as clearcut showing the need for performing more studies to uncover the factors responsible for high-quality NLG explanations.

Original language	English
Title of host publication	Proceedings of the 11th International Conference on Natural Language Generation
Publisher	Association for Computational Linguistics
Pages	177–182
Number of pages	6
ISBN (Print)	9781948087865
Publication status	Published - Nov 2018
Event	11th International Conference on Natural Language Generation 2018 - Tilburg, Netherlands Duration: 5 Nov 2018 → 8 Nov 2018

Conference

Conference	11th International Conference on Natural Language Generation 2018
Abbreviated title	INLG 2018
Country/Territory	Netherlands
City	Tilburg
Period	5/11/18 → 8/11/18

Access to Document

https://www.aclweb.org/anthology/W18-6522.pdfLicence: CC BY

Cite this

@inproceedings{1dfefcf19890450da73162812466c306,

title = "Towards making NLG a voice for interpretable Machine Learning",

abstract = "This paper presents a study to understand the issues related to using NLG to humanise explanations from a popular interpretable machine learning frameworkcalled LIME. Our study shows that selfreported rating of NLG explanation washigher than that for a non-NLG explanation. However, when tested for comprehension, the results were not as clearcut showing the need for performing more studies to uncover the factors responsible for high-quality NLG explanations.",

author = "James Forrest and Somayajulu Sripada and Wei Pang and George Coghill",

year = "2018",

month = nov,

language = "English",

isbn = "9781948087865",

pages = "177–182",

booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",

publisher = "Association for Computational Linguistics",

note = "11th International Conference on Natural Language Generation 2018, INLG 2018 ; Conference date: 05-11-2018 Through 08-11-2018",

}

Forrest, J, Sripada, S, Pang, W & Coghill, G 2018, Towards making NLG a voice for interpretable Machine Learning. in Proceedings of the 11th International Conference on Natural Language Generation. Association for Computational Linguistics, pp. 177–182, 11th International Conference on Natural Language Generation 2018, Tilburg, Netherlands, 5/11/18. <https://www.aclweb.org/anthology/W18-6522.pdf>

TY - GEN

T1 - Towards making NLG a voice for interpretable Machine Learning

AU - Forrest, James

AU - Sripada, Somayajulu

AU - Pang, Wei

AU - Coghill, George

PY - 2018/11

Y1 - 2018/11

N2 - This paper presents a study to understand the issues related to using NLG to humanise explanations from a popular interpretable machine learning frameworkcalled LIME. Our study shows that selfreported rating of NLG explanation washigher than that for a non-NLG explanation. However, when tested for comprehension, the results were not as clearcut showing the need for performing more studies to uncover the factors responsible for high-quality NLG explanations.

AB - This paper presents a study to understand the issues related to using NLG to humanise explanations from a popular interpretable machine learning frameworkcalled LIME. Our study shows that selfreported rating of NLG explanation washigher than that for a non-NLG explanation. However, when tested for comprehension, the results were not as clearcut showing the need for performing more studies to uncover the factors responsible for high-quality NLG explanations.

M3 - Conference contribution

SN - 9781948087865

SP - 177

EP - 182

BT - Proceedings of the 11th International Conference on Natural Language Generation

PB - Association for Computational Linguistics

T2 - 11th International Conference on Natural Language Generation 2018

Y2 - 5 November 2018 through 8 November 2018

ER -

Towards making NLG a voice for interpretable Machine Learning

Abstract

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