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 multidocument 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.