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    <titleInfo>
        <title>What Can We Learn from Noun Substitutions in Revision Histories?</title>
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    <name type="personal">
        <namePart type="given">Talita</namePart>
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            <title>Proceedings of the 28th International Conference on Computational Linguistics</title>
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            <namePart type="given">Donia</namePart>
            <namePart type="family">Scott</namePart>
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        <name type="personal">
            <namePart type="given">Nuria</namePart>
            <namePart type="family">Bel</namePart>
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        <name type="personal">
            <namePart type="given">Chengqing</namePart>
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    <abstract>In community-edited resources such as wikiHow, sentences are subject to revisions on a daily basis. Recent work has shown that resulting improvements over time can be modelled computationally, assuming that each revision contributes to the improvement. We take a closer look at a subset of such revisions, for which we attempt to improve a computational model and validate in how far the assumption that ‘revised means better’ actually holds. The subset of revisions considered here are noun substitutions, which often involve interesting semantic relations, including synonymy, antonymy and hypernymy. Despite the high semantic relatedness, we find that a supervised classifier can distinguish the revised version of a sentence from an original version with an accuracy close to 70%, when taking context into account. In a human annotation study, we observe that annotators identify the revised sentence as the ‘better version’ with similar performance. Our analysis reveals a fair agreement among annotators when a revision improves fluency. In contrast, noun substitutions that involve other lexical-semantic relationships are often perceived as being equally good or tend to cause disagreements. While these findings are also reflected in classification scores, a comparison of results shows that our model fails in cases where humans can resort to factual knowledge or intuitions about the required level of specificity.</abstract>
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    <identifier type="doi">10.18653/v1/2020.coling-main.117</identifier>
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        <date>2020-12</date>
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%0 Conference Proceedings
%T What Can We Learn from Noun Substitutions in Revision Histories?
%A Anthonio, Talita
%A Roth, Michael
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F anthonio-roth-2020-learn
%X In community-edited resources such as wikiHow, sentences are subject to revisions on a daily basis. Recent work has shown that resulting improvements over time can be modelled computationally, assuming that each revision contributes to the improvement. We take a closer look at a subset of such revisions, for which we attempt to improve a computational model and validate in how far the assumption that ‘revised means better’ actually holds. The subset of revisions considered here are noun substitutions, which often involve interesting semantic relations, including synonymy, antonymy and hypernymy. Despite the high semantic relatedness, we find that a supervised classifier can distinguish the revised version of a sentence from an original version with an accuracy close to 70%, when taking context into account. In a human annotation study, we observe that annotators identify the revised sentence as the ‘better version’ with similar performance. Our analysis reveals a fair agreement among annotators when a revision improves fluency. In contrast, noun substitutions that involve other lexical-semantic relationships are often perceived as being equally good or tend to cause disagreements. While these findings are also reflected in classification scores, a comparison of results shows that our model fails in cases where humans can resort to factual knowledge or intuitions about the required level of specificity.
%R 10.18653/v1/2020.coling-main.117
%U https://aclanthology.org/2020.coling-main.117
%U https://doi.org/10.18653/v1/2020.coling-main.117
%P 1359-1370

Markdown (Informal)

[What Can We Learn from Noun Substitutions in Revision Histories?](https://aclanthology.org/2020.coling-main.117) (Anthonio & Roth, COLING 2020)

• What Can We Learn from Noun Substitutions in Revision Histories? (Anthonio & Roth, COLING 2020)

ACL

• Talita Anthonio and Michael Roth. 2020. What Can We Learn from Noun Substitutions in Revision Histories?. In Proceedings of the 28th International Conference on Computational Linguistics, pages 1359–1370, Barcelona, Spain (Online). International Committee on Computational Linguistics.