Crowdsourcing language generation templates for dialogue systems

Abstract

We explore the use of crowdsourcing to generate natural language in spoken dialogue systems. We introduce a methodology to elicit novel templates from the crowd based on a dialogue seed corpus, and investigate the effect that the amount of surrounding dialogue context has on the generation task. Evaluation is performed both with a crowd and with a system developer to assess the naturalness and suitability of the elicited phrases. Results indicate that the crowd is able to provide reasonable and diverse templates within this methodology. More work is necessary before elicited templates can be automatically plugged into the system.