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Unsupervised Prediction of Acceptability Judgements

Conference paper
Authors Jey Han Lau
Alexander Clark
Shalom Lappin
Published in Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, Beijing, China, July 26-31 2015
Volume 53
Pages 1618–1628
ISBN 9781510808423
Publication year 2015
Published at
Pages 1618–1628
Language en
Keywords computational linguistics, natural language processing
Subject categories Computer and Information Science


In this paper we present the task of un- supervised prediction of speakers’ accept- ability judgements. We use a test set generated from the British National Corpus (BNC) containing both grammatical sentences and sentences containing a va- riety of syntactic infelicities introduced by round trip machine translation. This set was annotated for acceptability judgements through crowd sourcing. We trained a variety of unsupervised language mod- els on the original BNC, and tested them to see the extent to which they could pre- dict mean speakers’ judgements on the test set. To map probability to acceptability, we experimented with several normalisation functions to neutralise the effects of sentence length and word frequencies. We found encouraging results with the unsupervised models predicting acceptability across two different datasets. Our method- ology is highly portable to other domains and languages, and the approach has po- tential implications for the representation and the acquisition of linguistic knowledge.

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