[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content

Reproducibility of the Neural Vector Space Model via Docker

  • Conference paper
  • First Online:
Digital Libraries: The Era of Big Data and Data Science (IRCDL 2020)

Abstract

In this work we describe how Docker images can be used to enhance the reproducibility of Neural IR models. We report our results reproducing the Vector Space Neural Model (NVSM) and we release a CPU-based and a GPU-based Docker image. Finally, we present some insights about reproducing Neural IR models.

The full paper has been originally presented at the OSIRRC@SIGIR workshop [3].

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

Access this chapter

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 47.99
Price includes VAT (United Kingdom)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 59.99
Price includes VAT (United Kingdom)
  • 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

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://www.docker.com/.

  2. 2.

    https://github.com/usnistgov/trec_eval.

References

  1. Dür, A., Rauber, A., Filzmoser, P.: Reproducing a neural question answering architecture applied to the SQuAD benchmark dataset: challenges and lessons learned. In: Pasi, G., Piwowarski, B., Azzopardi, L., Hanbury, A. (eds.) ECIR 2018. LNCS, vol. 10772, pp. 102–113. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-76941-7_8

    Chapter  Google Scholar 

  2. Ferro, N., Fuhr, N., Maistro, M., Sakai, T., Soboroff, I.: Overview of CENTRE@CLEF 2019: sequel in the systematic reproducibility realm. In: Experimental IR Meets Multilinguality, Multimodality, and Interaction. Proceedings of the Tenth International Conference of the CLEF Association (CLEF 2019) (2019)

    Google Scholar 

  3. Ferro, N., Marchesin, S., Purpura, A., Silvello, G.: A docker-based replicability study of a neural information retrieval model. In: Proceedings of the Open-Source IR Replicability Challenge co-located with 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, OSIRRC@SIGIR 2019, vol. 2409, pp. 37–43. CEUR-WS.org (2019). http://ceur-ws.org/Vol-2409/docker05.pdf

  4. Freire, J., Fuhr, N., Rauber, A.: Reproducibility of data-oriented experiments in e-science (Dagstuhl Seminar 16041). In: Dagstuhl Reports, vol. 6, no. 1, pp. 108–159 (2016). https://doi.org/10.4230/DagRep.6.1.108, http://drops.dagstuhl.de/opus/volltexte/2016/5817

  5. Marchesin, S., Purpura, A., Silvello, G.: Focal elements of neural information retrieval models. an outlook through a reproducibility Study. Inf. Process. Manag. 34 (2019). print

    Google Scholar 

  6. Marchesin, S., Purpura, A., Silvello, G.: A neural vector space model implementation repository (2019). https://github.com/giansilv/NeuralIR/

  7. Sakai, T., Ferro, N., Soboroff, I., Zeng, Z., Xiao, P., Maistro, M.: Overview of the NTCIR-14 CENTRE task. In: Proceedings of the 14th NTCIR Conference on Evaluation of Information Access Technologies, Tokyo, Japan (2019)

    Google Scholar 

  8. Soboroff, I., Ferro, N., Sakai, T.: Overview of the TREC 2018 CENTRE track. In: The Twenty-Seventh Text REtrieval Conference Proceedings (TREC 2018) (2018)

    Google Scholar 

  9. Van Gysel, C., de Rijke, M., Kanoulas, E.: Neural vector spaces for unsupervised information retrieval. ACM Trans. Inf. Syst. 36(4), 38:1–38:25 (2018)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Nicola Ferro , Stefano Marchesin , Alberto Purpura or Gianmaria Silvello .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ferro, N., Marchesin, S., Purpura, A., Silvello, G. (2020). Reproducibility of the Neural Vector Space Model via Docker. In: Ceci, M., Ferilli, S., Poggi, A. (eds) Digital Libraries: The Era of Big Data and Data Science. IRCDL 2020. Communications in Computer and Information Science, vol 1177. Springer, Cham. https://doi.org/10.1007/978-3-030-39905-4_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-39905-4_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-39904-7

  • Online ISBN: 978-3-030-39905-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics