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

Abstract · Mar 31, 2017 21:03 ·

cs-cl

Arxiv Abstract

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

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.

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