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This paper investigates the ability of artificial neural networks to judge the grammatical acceptability of a sentence, with the goal of testing their linguistic competence.
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Allyson Ettinger, Ahmed Elgohary, and Philip Resnik. 2016 · 2016
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Grammaticality, acceptability, and probability: A probabilistic view of linguistic knowledge
Jey Han Lau, Alexander Clark, and Shalom Lappin. 2016 · 2016
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Assessing the ability of LSTMs to learn syntax-sensitive dependencies
Tal Linzen, Emmanuel Dupoux, and Yoav Goldberg. 2016 · 2016
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SNAP judgments: A small n acceptability paradigm (SNAP) for linguistic acceptability judgments
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Malka Rappaport Hovav and Beth Levin. 2008 · 2008
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Judging grammaticality: Experiments in sentence classification
Joachim Wagner, Jennifer Foster, and Josef van Genabith. 2009 · 2009
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Edward Gibson and Evelina Fedorenko. 2010 · 2010
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A comparison of informal and formal acceptability judgments using a random sample from Linguistic Inquiry 2001–2010
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Setting the empirical record straight: Acceptability judgments appear to be reliable, robust, and replicable
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