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        <title>Sentence Simplification for Semantic Role Labelling and Information Extraction</title>
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        <namePart type="given">Richard</namePart>
        <namePart type="family">Evans</namePart>
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            <title>Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)</title>
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        <name type="personal">
            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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        <name type="personal">
            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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    <abstract>In this paper, we report on the extrinsic evaluation of an automatic sentence simplification method with respect to two NLP tasks: semantic role labelling (SRL) and information extraction (IE). The paper begins with our observation of challenges in the intrinsic evaluation of sentence simplification systems, which motivates the use of extrinsic evaluation of these systems with respect to other NLP tasks. We describe the two NLP systems and the test data used in the extrinsic evaluation, and present arguments and evidence motivating the integration of a sentence simplification step as a means of improving the accuracy of these systems. Our evaluation reveals that their performance is improved by the simplification step: the SRL system is better able to assign semantic roles to the majority of the arguments of verbs and the IE system is better able to identify fillers for all IE template slots.</abstract>
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    <identifier type="doi">10.26615/978-954-452-056-4_033</identifier>
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        <url>https://aclanthology.org/R19-1033/</url>
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        <date>2019-09</date>
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%0 Conference Proceedings
%T Sentence Simplification for Semantic Role Labelling and Information Extraction
%A Evans, Richard
%A Orasan, Constantin
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
%D 2019
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F evans-orasan-2019-sentence
%X In this paper, we report on the extrinsic evaluation of an automatic sentence simplification method with respect to two NLP tasks: semantic role labelling (SRL) and information extraction (IE). The paper begins with our observation of challenges in the intrinsic evaluation of sentence simplification systems, which motivates the use of extrinsic evaluation of these systems with respect to other NLP tasks. We describe the two NLP systems and the test data used in the extrinsic evaluation, and present arguments and evidence motivating the integration of a sentence simplification step as a means of improving the accuracy of these systems. Our evaluation reveals that their performance is improved by the simplification step: the SRL system is better able to assign semantic roles to the majority of the arguments of verbs and the IE system is better able to identify fillers for all IE template slots.
%R 10.26615/978-954-452-056-4_033
%U https://aclanthology.org/R19-1033/
%U https://doi.org/10.26615/978-954-452-056-4_033
%P 285-294

Markdown (Informal)

[Sentence Simplification for Semantic Role Labelling and Information Extraction](https://aclanthology.org/R19-1033/) (Evans & Orasan, RANLP 2019)

• Sentence Simplification for Semantic Role Labelling and Information Extraction (Evans & Orasan, RANLP 2019)

ACL

• Richard Evans and Constantin Orasan. 2019. Sentence Simplification for Semantic Role Labelling and Information Extraction. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 285–294, Varna, Bulgaria. INCOMA Ltd..