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NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F21%3APU142944" target="_blank" >RIV/00216305:26230/21:PU142944 - isvavai.cz</a>

  • Result on the web

    <a href="http://proceedings.mlr.press/v133/min21a/min21a.pdf" target="_blank" >http://proceedings.mlr.press/v133/min21a/min21a.pdf</a>

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned

  • Original language description

    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.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

    R - Projekt Ramcoveho programu EK

Others

  • Publication year

    2021

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Proceedings of the NeurIPS 2020 Competition and Demonstration Track

  • ISBN

  • ISSN

    2640-3498

  • e-ISSN

  • Number of pages

    25

  • Pages from-to

    86-111

  • Publisher name

    Proceedings of Machine Learning Research

  • Place of publication

    online

  • Event location

    online

  • Event date

    Dec 6, 2020

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article