A predictive approach to help-desk response generation

Authors:
Yuval Marom;Ingrid Zukerman
Affiliations:
Faculty of Information Technology, Monash University, Clayton, Victoria, Australia;Faculty of Information Technology, Monash University, Clayton, Victoria, Australia
Venue:
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Year:
2007

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An empirical study of corpus-based response automation methods for an e-mail-based help-desk domain

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Abstract

We are developing a corpus-based approach for the prediction of help-desk responses from features in customers' emails, where responses are represented at two levels of granularity: document and sentence. We present an automatic and human-based evaluation of our system's responses. The automatic evaluation involves textual comparisons between generated responses and responses composed by help-desk operators. Our results showthat both levels of granularity produce good responses, addressing inquiries of different kinds. The human-based evaluation measures response informativeness, and confirms our conclusion that both levels of granularity produce useful responses.