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


This paper proposes a new dataset, Frames, composed of 1369 human-human dialogues with an average of 15 turns per dialogue. This corpus contains goal-oriented dialogues between users who are given some constraints to book a trip and assistants who search a database to find appropriate trips. The users exhibit complex decision-making behaviour which involve comparing trips, exploring different options, and selecting among the trips that were discussed during the dialogue. To drive research on dialogue systems towards handling such behaviour, we have annotated and released the dataset and we propose in this paper a task called frame tracking. This task consists of keeping track of different semantic frames throughout each dialogue. We propose a rule-based baseline and analyse the frame tracking task through this baseline.


  title     = {{F}rames: A Corpus for Adding Memory to Goal-Oriented Dialogue Systems},
  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},
  booktitle = {Proceedings of the 18th Annual {SIG}dial Meeting on Discourse and Dialogue},
  month     = {August},
  year      = {2017},
  address   = {Saarbr{\"u}cken, Germany},
  publisher = {Association for Computational Linguistics},
  url       = {},
  doi       = {10.18653/v1/W17-5526},
  pages     = {207--219}