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Probing Classifiers: Promises, Shortcomings, and Advances

Computational linguistics - Association for Computational Linguistics, 2022-04, Vol.48 (1), p.207-219 [Peer Reviewed Journal]

2022. This work is published under https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 0891-2017 ;EISSN: 1530-9312 ;DOI: 10.1162/coli_a_00422

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  • Title:
    Probing Classifiers: Promises, Shortcomings, and Advances
  • Author: Belinkov, Yonatan
  • Subjects: Artificial neural networks ; Classifiers ; Natural language processing
  • Is Part Of: Computational linguistics - Association for Computational Linguistics, 2022-04, Vol.48 (1), p.207-219
  • Description: Probing classifiers have emerged as one of the prominent methodologies for interpreting and analyzing deep neural network models of natural language processing. The basic idea is simple—a classifier is trained to predict some linguistic property from a model’s representations—and has been used to examine a wide variety of models and properties. However, recent studies have demonstrated various methodological limitations of this approach. This squib critically reviews the probing classifiers framework, highlighting their promises, shortcomings, and advances.
  • Publisher: One Broadway, 12th Floor, Cambridge, Massachusetts 02142, USA: MIT Press
  • Language: English
  • Identifier: ISSN: 0891-2017
    EISSN: 1530-9312
    DOI: 10.1162/coli_a_00422
  • Source: DOAJ Directory of Open Access Journals
    Alma/SFX Local Collection
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