Frames: A Corpus for Adding Memory to Goal-Oriented Dialogue Systems

03/31/2017 ∙ by Layla El Asri, et al. ∙ 0

This paper presents the Frames dataset (Frames is available at, a corpus of 1369 human-human dialogues with an average of 15 turns per dialogue. We developed this dataset to study the role of memory in goal-oriented dialogue systems. Based on Frames, we introduce a task called frame tracking, which extends state tracking to a setting where several states are tracked simultaneously. We propose a baseline model for this task. We show that Frames can also be used to study memory in dialogue management and information presentation through natural language generation.



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