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ConvAI Dataset of Topic-Oriented Human-to-Chatbot Dialogues

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The NIPS '17 Competition: Building Intelligent Systems

Abstract

This paper contains the description and the analysis of the dataset collected during the Conversational Intelligence Challenge (ConvAI) which took place in 2017. During the evaluation round we collected over 4,000 dialogues from 10 chatbots and 1,000 volunteers. Here we provide the dataset statistics and outline some possible improvements for future data collection experiments.

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Notes

  1. 1.

    The dataset is available at http://convai.io/2017/data/

  2. 2.

    http://turing.tilda.ws/

  3. 3.

    https://nips.cc/Conferences/2017

References

  • Burtsev, M., Logacheva, V., Malykh, V., Serban, I., Lowe, R., Prabhumoye, S., Black, A. W., Rudnicky, A., and Bengio, Y. (2018). The First Conversational Intelligence Challenge. NIPS 2017 Competition track Springer Proceedings.

    Google Scholar 

  • Lowe, R., Noseworthy, M., Serban, I. V., Angelard-Gontier, N., Bengio, Y., and Pineau, J. (2017). Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses. Acl, pages 1–19.

    Google Scholar 

  • Rajpurkar, P., Zhang, J., Lopyrev, K., and Liang, P. (2016). SQuAD: 100,000+ Questions for Machine Comprehension of Text. Emnlp, (ii):2383–2392.

    Google Scholar 

  • Serban, I. V., Lowe, R., Charlin, L., and Pineau, J. (2015). A Survey of Available Corpora for Building Data-Driven Dialogue Systems. CoRR, page 46.

    Google Scholar 

  • Serban, I. V., Sordoni, A., Lowe, R., Charlin, L., Pineau, J., Courville, A., and Bengio, Y. (2016). A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues. Proceedings of the Advances in Neural Information Processing Systems 29 (NIPS 2016), pages 1–14.

    Google Scholar 

  • Yu, Z., Xu, Z., Black, A. W., and Rudnicky, A. I. (2016). Chatbot Evaluation and Database Expansion via Crowdsourcing. WOCHAT workshop.

    Google Scholar 

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Acknowledgements

The work was supported by National Technology Initiative and PAO Sberbank project ID 0000000007417F630002.

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Correspondence to Varvara Logacheva .

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Logacheva, V., Burtsev, M., Malykh, V., Polulyakh, V., Seliverstov, A. (2018). ConvAI Dataset of Topic-Oriented Human-to-Chatbot Dialogues. In: Escalera, S., Weimer, M. (eds) The NIPS '17 Competition: Building Intelligent Systems. The Springer Series on Challenges in Machine Learning. Springer, Cham. https://doi.org/10.1007/978-3-319-94042-7_3

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  • DOI: https://doi.org/10.1007/978-3-319-94042-7_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-94041-0

  • Online ISBN: 978-3-319-94042-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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