Which Step Do I Take First? Troubleshooting with Bayesian Models

Annie Louis, Mirella Lapata


Abstract
Online discussion forums and community question-answering websites provide one of the primary avenues for online users to share information. In this paper, we propose text mining techniques which aid users navigate troubleshooting-oriented data such as questions asked on forums and their suggested solutions. We introduce Bayesian generative models of the troubleshooting data and apply them to two interrelated tasks: (a) predicting the complexity of the solutions (e.g., plugging a keyboard in the computer is easier compared to installing a special driver) and (b) presenting them in a ranked order from least to most complex. Experimental results show that our models are on par with human performance on these tasks, while outperforming baselines based on solution length or readability.
Anthology ID:
Q15-1006
Volume:
Transactions of the Association for Computational Linguistics, Volume 3
Month:
Year:
2015
Address:
Venue:
TACL
SIG:
Publisher:
Note:
Pages:
73–85
Language:
URL:
https://www.aclweb.org/anthology/Q15-1006
DOI:
10.1162/tacl_a_00123
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PDF:
http://aclanthology.lst.uni-saarland.de/Q15-1006.pdf
Video:
 https://vimeo.com/160938279