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The Influence of Context on Sentence Acceptability Judgements

Conference paper
Authors Jean-Philippe Bernardy
Shalom Lappin
Jey Han Lau
Published in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Short Papers). Melbourne, Australia, July 15 - 20, 2018
ISBN 978-1-948087-34-6
Publisher Association of Computational Linguistics
Place of publication Stroudsburg, PA
Publication year 2018
Published at Department of Philosophy, Linguistics and Theory of Science
Language en
Links aclweb.org/anthology/P18-2073
https://gup.ub.gu.se/file/207505
Keywords Deep Learning of acceptability judgments, document context
Subject categories Computational linguistics, Language Technology (Computational Linguistics)

Abstract

We investigate the influence that document context exerts on human acceptability judgements for English sentences, via two sets of experiments. The first compares ratings for sentences presented on their own with ratings for the same set of sentences given in their document contexts. The second assesses the accuracy with which two types of neural models — one that incorporates context during training and one that does not — predict these judgements. Our results indicate that: (1) context improves acceptability ratings for ill-formed sentences, but also reduces them for well-formed sentences; and (2) context helps unsupervised systems to model acceptability.

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