Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue |
Published by Association for Computational Linguistics
This paper proposes a new dataset, Frames, composed of 1369 human-human dialogues with an average of 15 turns per dialogue. This corpus contains goal-oriented dialogues between users who are given some constraints to book a trip and assistants who search a database to find appropriate trips. The users exhibit complex decision-making behaviour which involve comparing trips, exploring different options, and selecting among the trips that were discussed during the dialogue. To drive research on dialogue systems towards handling such behaviour, we have annotated and released the dataset and we propose in this paper a task called frame tracking. This task consists of keeping track of different semantic frames throughout each dialogue. We propose a rule-based baseline and analyse the frame tracking task through this baseline.
Frames is a dataset designed to encourage research towards conversational agents which can support decision-making in complex settings, in this case - booking a vacation including flights and a hotel. More than just searching a database, we believe the next generation of conversational agents will need to help users explore a database, compare items, and reach a decision.
