Frames: A Corpus for Adding Memory to Goal-Oriented Dialogue Systems
This work addresses the problem of memory in goal-oriented dialogue systems for researchers, but it is incremental as it builds on existing state tracking with a new dataset and task.
The paper introduces the Frames dataset, a corpus of 1369 human-human dialogues with an average of 15 turns per dialogue, to study memory in goal-oriented dialogue systems, and proposes a baseline model for the frame tracking task.
This paper presents the Frames dataset (Frames is available at http://datasets.maluuba.com/Frames), 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.