Publication Details

NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned

MIN, S.; FAJČÍK, M.; DOČEKAL, M.; ONDŘEJ, K.; SMRŽ, P. NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned. Proceedings of the NeurIPS 2020 Competition and Demonstration Track. Proceedings of Machine Learning Research. online: Proceedings of Machine Learning Research, 2021. p. 86-111. ISSN: 2640-3498.
Czech title
Soutěž NeurIPS 2020 EfficientQA: Systémy, analýzy a získané lekce
Type
conference paper
Language
English
Authors
MIN, S.
Fajčík Martin, Ing., Ph.D. (DCGM)
Dočekal Martin, Ing. (DCGM)
Ondřej Karel, Ing. (FIT)
Smrž Pavel, doc. RNDr., Ph.D. (DCGM)
and others
URL
Keywords

question answering, QA, ODQA, efficientQA, memory, disk memory, budget, efficient parameter, retrieval corpora

Abstract

We review the EfficientQA competition from NeurIPS 2020. The competition focused on open-domain question answering (QA), where systems take natural language questions as input and return natural language answers. The aim of the competition was to build systems that can predict correct answers while also satisfying strict on-disk memory budgets. These memory budgets were designed to encourage contestants to explore the trade-off between storing retrieval corpora or the parameters of learned models. In this report, we describe the motivation and organization of the competition, review the best submissions, and analyze system predictions to inform a discussion of evaluation for open-domain QA.

Published
2021
Pages
86–111
Proceedings
Proceedings of the NeurIPS 2020 Competition and Demonstration Track
Series
Proceedings of Machine Learning Research
Volume
133
Number
133
Publisher
Proceedings of Machine Learning Research
Place
online
BibTeX 
@inproceedings{BUT175821,
  author="MIN, S. and FAJČÍK, M. and DOČEKAL, M. and ONDŘEJ, K. and SMRŽ, P.",
  title="NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned",
  booktitle="Proceedings of the NeurIPS 2020 Competition and Demonstration Track",
  year="2021",
  series="Proceedings of Machine Learning Research",
  volume="133",
  number="133",
  pages="86--111",
  publisher="Proceedings of Machine Learning Research",
  address="online",
  issn="2640-3498",
  url="http://proceedings.mlr.press/v133/min21a/min21a.pdf"
}
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