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 selfreported rating of NLG explanation was
higher 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.
called LIME. Our study shows that selfreported rating of NLG explanation was
higher 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 Natural Language Generation Conference |
| Editors | Emiel Krahmer, Albert Gatt, Martijn Goudbeek |
| Publisher | Association for Computational Linguistics (ACL) |
| Pages | 177-182 |
| Number of pages | 6 |
| ISBN (Print) | 9781948087865 |
| Publication status | Published - 30 Nov 2018 |
| Event | 11th International Conference on Natural Language Generation (INLG 2018) - Tilburg University, Tilburg, Netherlands Duration: 5 Nov 2018 → 8 Nov 2018 |
Conference
| Conference | 11th International Conference on Natural Language Generation (INLG 2018) |
|---|---|
| Country | Netherlands |
| City | Tilburg |
| Period | 5/11/18 → 8/11/18 |
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Cite this
Forrest, J., Sripada, S., Pang, W., & Coghill, G. (2018). Towards making NLG a voice for interpretable Machine Learning. In E. Krahmer, A. Gatt, & M. Goudbeek (Eds.), Proceedings of The 11th International Natural Language Generation Conference (pp. 177-182). [W18-6522] Association for Computational Linguistics (ACL).