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.
@article{arxiv.1704.00057,
title = {Frames: A Corpus for Adding Memory to Goal-Oriented Dialogue Systems},
author = {Layla El Asri and Hannes Schulz and Shikhar Sharma and Jeremie Zumer and Justin Harris and Emery Fine and Rahul Mehrotra and Kaheer Suleman},
journal= {arXiv preprint arXiv:1704.00057},
year = {2017}
}