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dc.contributor.authorLau, JH
dc.contributor.authorSantos Armendariz, C
dc.contributor.authorLappin, S
dc.contributor.authorPurver, M
dc.contributor.authorShu, C
dc.date.accessioned2020-04-16T09:00:08Z
dc.date.available2020-03-30
dc.date.available2020-04-16T09:00:08Z
dc.date.issued2020
dc.identifier.issn2307-387X
dc.identifier.urihttps://qmro.qmul.ac.uk/xmlui/handle/123456789/63602
dc.description.abstractWe 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 which 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 modelling of text and discourse.en_US
dc.publisherMIT Pressen_US
dc.relation.ispartofTransactions of the Association for Computational Linguistics
dc.titleHow Furiously Can Colourless Green Ideas Sleep? Sentence Acceptability in Contexten_US
dc.typeArticleen_US
dc.rights.holder© The Author(s) 2020. Publisher by MIT Press
pubs.notesNot knownen_US
pubs.publication-statusAccepteden_US
dcterms.dateAccepted2020-03-30
rioxxterms.funderDefault funderen_US
rioxxterms.identifier.projectDefault projecten_US
qmul.funderEMBEDDIA: Cross-Lingual Embeddings for Less-Represented Languages in European News Media::European Commissionen_US
qmul.funderEMBEDDIA: Cross-Lingual Embeddings for Less-Represented Languages in European News Media::European Commissionen_US


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