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


Layla El Asri, Hannes Schulz, Shikhar Sharma, Jeremie Zumer, Justin Harris, Emery Fine, Rahul Mehrotra, Kaheer Suleman

Microsoft Maluuba

Abstract

This paper presents the Frames dataset, 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.

BibTeX

            @article{elasri2017frames,
  author  = {El Asri, Layla and
             Schulz, Hannes and
             Sharma, Shikhar and
             Zumer, Jeremie and
             Harris, Justin and
             Fine, Emery and
             Mehrotra, Rahul and
             Suleman, Kaheer},
  title   = {Frames: A Corpus for Adding Memory to Goal-Oriented Dialogue Systems},
  journal = {CoRR},
  volume  = {abs/1704.00057},
  year    = {2017},
  url     = {https://arxiv.org/abs/1704.00057}
}
          

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