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A report on the Automatic Evaluation of Scientific Writing Shared Task.

Conference paper
Authors Vidas Daudaravicius
Rafael E. Banchs
Elena Volodina
Courtney Napoles
Published in Workshop on Innovative Use of NLP for Building Educational Applications, June 16, 2016, San Diego, CA, USA
ISBN 978-1-941643-83-9
Publication year 2016
Published at Department of Swedish
Language en
Keywords NLP, machine learning, automatic evaluation of writing, academic writing, shared task
Subject categories General Language Studies and Linguistics, Language Technology (Computational Linguistics)


The Automated Evaluation of Scientific Writing, or AESW, is the task of identifying sentences in need of correction to ensure their appropriateness in a scientific prose. The data set comes from a professional editing company, VTeX, with two aligned versions of the same text – before and after editing – and covers a variety of textual infelicities that proofreaders have edited. While previous shared tasks focused solely on grammatical errors (Dale and Kilgarriff, 2011; Dale et al., 2012; Ng et al., 2013; Ng et al., 2014), this time edits cover other types of linguistic misfits as well, including those that almost certainly could be interpreted as style issues and similar “matters of opinion”. The latter arise because of different language editing traditions, experience, and the absence of uniform agreement on what “good” scientific language should look like. Initiating this task, we expected the participating teams to help identify the characteristics of “good” scientific language, and help create a consensus of which language improvements are acceptable (or necessary). Six participating teams took on the challenge.

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