文章基本信息

标题：Probing Classifiers: Promises, Shortcomings, and Advances
本地全文：下载
作者：Yonatan Belinkov
期刊名称：Computational Linguistics
印刷版ISSN：0891-2017
电子版ISSN：1530-9312
出版年度：2022
卷号：48
期号：1
页码：207-219
DOI：10.1162/coli_a_00422
语种：English
出版社：MIT Press
摘要：AbstractProbing 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.