A Meta-learning Approach for Selecting between Response Automation Strategies in a Help-desk Domain

We present a corpus-based approach for the automation of help-desk responses to users’ email requests. Automation is performed on the basis of the similarity between 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 paper, which introduces a meta-learning mechanism that selects between different information-gathering strategies, 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.

• Type: Conference
• Year: 2007
• Where: AAAI
• Authors: Yuval Marom, Ingrid Zukerman, Nathalie Japkowicz
• Keywords: AAAI 2007, Corpus-based Approach, Help-desk Responses, Intelligent Agents, Random Strategy-selection Policy