Recurrent Neural Learning for Helpdesk Call Routing

Sheila Garfield, Stefan Wermter

Abstract: In the past, recurrent networks have been used mainly in neurocognitive or psycholinguistically oriented approaches of language processing. Here we examine recurrent neural networks for their potential in a difficult spoken language classification task. This paper describes an approach to learning classification of recorded operator assistance telephone utterances. We explore simple recurrent networks using a large, unique telecommunication corpus of spontaneous spoken language. Performance of the network indicates that a semantic SRN network is quite useful for learning classification of spontaneous spoken language in a robust manner, which may lead to their use in helpdesk call routing.