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How Furiously Can Colorless Green Ideas Sleep? Sentence Acceptability in Context

Journal article
Authors Jey Han Lau
Carlos Armendariz
Shalom Lappin
Matthew Purver
Chang Shu
Published in Transactions of the Association of Computational Linguistics
Volume 8
Pages 296-310
Publication year 2020
Published at Department of Philosophy, Linguistics and Theory of Science
Pages 296-310
Language en
Keywords Sentence acceptability, context, deep learning
Subject categories Computer science, Computational linguistics


We study the influence of context on sentence acceptability. First we compare the acceptability ratings of sentences judged in isolation, with a relevant context, and with an irrelevant context. Our results show that context induces a cognitive load for humans, which compresses the distribution of ratings. Moreover, in relevant contexts we observe a discourse coherence effect that uniformly raises acceptability. Next, we test unidirectional and bidirectional language models in their ability to predict acceptability ratings. The bidirectional models show very promising results, with the best model achieving a new state-of-the-art for unsupervised acceptability prediction. The two sets of experiments provide insights into the cognitive aspects of sentence processing and central issues in the computational modeling of text and discourse.

Page Manager: Webmaster|Last update: 9/11/2012

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