Linguistic Analysis of Pretrained Sentence Encoders with Acceptability Judgments

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How Context Affects Language Models' Factual Predictions (2020, background): “…as syntax trees [Marvin and Linzen, 2018, Hewitt and Manning, 2019, Vig and Belinkov, 2019], negative polarity items [Warstadt and Bowman, 2019, Warstadt et al., 2019], semantic fragments [Richardson et al., 2019], function words [Kim et al., 2019], and many other linguistic phenomena [Tenney…”
BLiMP: The Benchmark of Linguistic Minimal Pairs for English (2019, neutral): “…classiﬁcation, and there is no generally accepted method for obtaining binary acceptability predictions from unsupervised models like LMs. 2 Warstadt and Bowman (2019) measure phenomenon-speciﬁc performance on CoLA for several pretrained sentence encoding models: an LSTM, GPT (Radford et…”
Investigating BERT’s Knowledge of Language: Five Analysis Methods with NPIs (2019, methodology): “Warstadt et al. (2018) train models on this task using the CoLA corpus of acceptability judgments as a method for evaluating domain general grammatical knowledge, and Warstadt and Bowman (2019) analyze how these domain general classifiers perform on specific linguistic phenomena.”