Identifying Topic Shift and Topic Shading in Switchboard
Brendan Spillane, Vincent Wade, Emer Gilmartin, Christian Saam, Leigh Clark, Benjamin R. Cowan · Arrow@dit (Dublin Institute of Technology) · 2018
This paper highlights some of the ongoing work on the ADELE project, namely the identification and annotation of topic shift and topic shading in the Switchboard-1 Release-2 corpus. The purpose of this is to train an Artificial Neural Network to create a digital companion for the elderly that can communicate through informal,yet informed social dialogue, on a variety of topics of interest to a user over a prolonged time scale. To this end the project is focussing on topic shift and shading, the mechanisms which underpin the development of such conversations [6,8]. In the past, dialogue systems have predominantly focussed on practical tasks due to the complexity of modelling realistic everyday social talk [1]. With increasing awareness of the need for home robots and virtual home care agents to help assist in the provision of care for a rapidly ageing population, it is necessary to develop a more caring, involved, and personalised virtual care agent capable of such social dialogue.