A Meta-Learning Approach for Selecting Between Response Automation Strategies in a Help-Desk Domain
Abstract
We present a corpus-based approach for the automation of help-desk responses to users ’ email requests. Au-tomation is performed on the basis of the similarity be-tween a request and previous requests, which affects both the content included in a response and the strategy used to produce it. The latter is the focus of this pa-per, which introduces a meta-learning mechanism that selects between different information-gathering strate-gies, such as document retrieval and multi-document summarization. Our results show that this mechanism outperforms a random strategy-selection policy, and performs competitively with a gold baseline that always selects the best strategy.
Cite
Text
Marom et al. "A Meta-Learning Approach for Selecting Between Response Automation Strategies in a Help-Desk Domain." AAAI Conference on Artificial Intelligence, 2007.Markdown
[Marom et al. "A Meta-Learning Approach for Selecting Between Response Automation Strategies in a Help-Desk Domain." AAAI Conference on Artificial Intelligence, 2007.](https://mlanthology.org/aaai/2007/marom2007aaai-meta/)BibTeX
@inproceedings{marom2007aaai-meta,
title = {{A Meta-Learning Approach for Selecting Between Response Automation Strategies in a Help-Desk Domain}},
author = {Marom, Yuval and Zukerman, Ingrid and Japkowicz, Nathalie},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2007},
pages = {907-912},
url = {https://mlanthology.org/aaai/2007/marom2007aaai-meta/}
}