Skip to main content

Crowd-Sourced Collection of Task-Oriented Human-Human Dialogues in a Multi-domain Scenario

  • Conference paper
  • First Online:
  • 787 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11697))

Abstract

There is a lack of high-quality corpora for the purposes of training task-oriented, end-to-end dialogue systems. This paper describes a dialogue collection process which used crowd-sourcing and a Wizard-of-Oz set-up to collect written human-human dialogues for a task-oriented, multi-domain scenario. The context is a tourism agency, where users try to select the more desired hotel, restaurant, museum or shop. To respond to users, wizards were assisted by an exploratory system supporting Preference-enriched Faceted Search. An important aspect was the translation of user intent to a number of actions (hard or soft-constraints) by wizards. The main goal was to collect dialogues as realistic as possible between a user and an operator, suitable for training end-to-end dialogue systems. This work describes the experiences made, the options and the decisions taken to minimize the human effort and budget, along with the tools used and developed, and describes in detail the resulting dialogue collection.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    https://datasets.maluuba.com/Frames/.

  2. 2.

    https://github.com/xiul-msr/e2e_dialog_challenge.

  3. 3.

    https://xiul-msr.github.io/e2e_dialog_challenge/slides/MS_dialog_challenge_result_outlook_sungjin.pptx.

  4. 4.

    https://www.microworkers.com.

  5. 5.

    https://www.mturk.com.

  6. 6.

    https://www.tawk.to.

  7. 7.

    http://www.ics.forth.gr/isl/Hippalus.

  8. 8.

    http://www.w3.org/TR/rdf-schema.

References

  1. Asri, L.E., et al.: Frames: a corpus for adding memory to goal-oriented dialogue systems. In: Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue (2017)

    Google Scholar 

  2. Budzianowski, P., et al.: MultiWOZ - A large-scale multi-domain wizard-of-oz dataset for task-oriented dialogue modelling. [Dataset] (2018)

    Google Scholar 

  3. Dahlbäck, N., Jönsson, A., Ahrenberg, L.: Wizard of Oz studies: why and how. In: Proceedings of the 1st International Conference on Intelligent User Interfaces, IUI 1993, pp. 193–200. ACM, New York (1993)

    Google Scholar 

  4. Kelley, J.F.: An iterative design methodology for user-friendly natural language office information applications. ACM Trans. Inf. Syst. (TOIS) 2(1), 26–41 (1984)

    Article  Google Scholar 

  5. Li, X., Panda, S., Liu, J.J., Gao, J.: Microsoft dialogue challenge: Building end-to-end task-completion dialogue systems (2018)

    Google Scholar 

  6. Lowe, R., Pow, N., Serban, I., Pineau, J.: The ubuntu dialogue corpus: a large dataset for research in unstructured multi-turn dialogue systems. In: Proceedings of the 16th Annual SIGdial Meeting on Discourse and Dialogue (2015)

    Google Scholar 

  7. Mrkšić, N., Ó Séaghdha, D., Wen, T.H., Thomson, B., Young, S.: The neural belief tracker: Data-driven dialogue state tracking. In: ACL, Vancouver, Canada (2017)

    Google Scholar 

  8. Papadakos, P., Tzitzikas, Y.: Hippalus: preference-enriched faceted exploration. In: EDBT/ICDT Workshops, vol. 172 (2014)

    Google Scholar 

  9. Peer, E., Brandimarte, L., Samat, S., Acquisti, A.: Beyond the Turk: alternative platforms for crowdsourcing behavioral research. J. Exp. Soc. Psychol. 70, 153–163 (2017). https://doi.org/10.2139/ssrn.2594183

    Article  Google Scholar 

  10. Petrik, S.: Wizard of Oz Experiments on Speech Dialogue Systems. Diploma thesis, Technical University of Graz (2004)

    Google Scholar 

  11. Ritter, A., Cherry, C., Dolan, W.B.: Unsupervised modelling of Twitter conversations. In: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 172–180 (2010)

    Google Scholar 

  12. Schrading, N., Alm, C., Ptucha, R., Homan, C.: An analysis of domestic abuse discourse on reddit. In: Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 2577–2583 (2015)

    Google Scholar 

  13. Serban, I., Sordoni, A., Bengio, Y., Courville, A.C., Pineau, J.: Hierarchical neural network generative models for movie dialogues. ArXiv e-prints (2015)

    Google Scholar 

  14. Serban, I.V., Lowe, R., Henderson, P., Charlin, L., Pineau, J.: A survey of available corpora for building data-driven dialogue systems: the journal version. Dialogue Discourse 9(1), 1–49 (2018)

    Google Scholar 

  15. Shang, L., Lu, Z., Li, H.: Neural responding machine for short-text conversation. In: ACL, Beijing, China, pp. 1577–1586 (2015)

    Google Scholar 

  16. Tzitzikas, Y., Papadakos, P.: Interactive exploration of multi-dimensional and hierarchical information spaces with real-time preference elicitation. Fundamenta Informaticae 122(4), 357–399 (2013)

    MathSciNet  MATH  Google Scholar 

  17. Vakharia, D., Lease, M.: Beyond Mechanical Turk: an analysis of paid crowd work platforms. In: Proceedings of the iConference (2015)

    Google Scholar 

  18. Vinyals, O., Le, Q.V.: A neural conversational model. In: ICML Deep Learning Workshop, Lille, France (2015)

    Google Scholar 

  19. Wen, T.H., et al.: A network-based end-to-end trainable task-oriented dialogue system. In: EACL, Valencia, Spain, pp. 438–449 (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Norbert Braunschweiler .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Braunschweiler, N., Papadakos, P., Kotti, M., Marketakis, Y., Tzitzikas, Y. (2019). Crowd-Sourced Collection of Task-Oriented Human-Human Dialogues in a Multi-domain Scenario. In: Ekštein, K. (eds) Text, Speech, and Dialogue. TSD 2019. Lecture Notes in Computer Science(), vol 11697. Springer, Cham. https://doi.org/10.1007/978-3-030-27947-9_34

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-27947-9_34

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-27946-2

  • Online ISBN: 978-3-030-27947-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics