Should i tell all?: an experiment on conciseness in spoken dialogue

Spoken dialogue systems have a strong requirement to produce concise and informative utterances. While interacting over a phone, users must both understand the system’s utterances, and remember important facts that the system is providing. Thus most dialogue systems implement some combination of different techniques for (1) option selection: pruning the set of options; (2) information selection: selecting a subset of information to present about each option; (3) aggregation: combining multiple items of information succinctly. We first describe how user models based on multi-attribute decision theory support domain-independent algorithms for both option selection and information selection. We then describe experiments to determine an optimal level of conciseness in information selection, i.e. how much information to include for an option. Our results show that (a) users are highly oriented to utterance conciseness; (b) the information selection algorithm is highly consistent with user’s judgments of conciseness; and (c) the appropriate level of conciseness is both user and dialogue strategy dependent.