Frames: A Corpus for Adding Memory to Goal-Oriented Dialogue Systems
Abstract
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.
- Publication:
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arXiv e-prints
- Pub Date:
- March 2017
- DOI:
- 10.48550/arXiv.1704.00057
- arXiv:
- arXiv:1704.00057
- Bibcode:
- 2017arXiv170400057E
- Keywords:
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- Computer Science - Computation and Language