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        <title>Improving Grammatical Error Correction for Multiword Expressions</title>
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        <namePart type="given">Shiva</namePart>
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        <namePart type="given">Christopher</namePart>
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    <name type="personal">
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        <dateIssued>2022-06</dateIssued>
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            <title>Proceedings of the 18th Workshop on Multiword Expressions @LREC2022</title>
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            <namePart type="given">Archna</namePart>
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            <namePart type="given">Paul</namePart>
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    <abstract>Grammatical error correction (GEC) is the task of automatically correcting errors in text. It has mainly been developed to assist language learning, but can also be applied to native text. This paper reports on preliminary work in improving GEC for multiword expression (MWE) error correction. We propose two systems which incorporate MWE information in two different ways: one is a multi-encoder decoder system which encodes MWE tags in a second encoder, and the other is a BART pre-trained transformer-based system that encodes MWE representations using special tokens. We show improvements in correcting specific types of verbal MWEs based on a modified version of a standard GEC evaluation approach.</abstract>
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%0 Conference Proceedings
%T Improving Grammatical Error Correction for Multiword Expressions
%A Taslimipoor, Shiva
%A Bryant, Christopher
%A Yuan, Zheng
%Y Bhatia, Archna
%Y Cook, Paul
%Y Taslimipoor, Shiva
%Y Garcia, Marcos
%Y Ramisch, Carlos
%S Proceedings of the 18th Workshop on Multiword Expressions @LREC2022
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F taslimipoor-etal-2022-improving
%X Grammatical error correction (GEC) is the task of automatically correcting errors in text. It has mainly been developed to assist language learning, but can also be applied to native text. This paper reports on preliminary work in improving GEC for multiword expression (MWE) error correction. We propose two systems which incorporate MWE information in two different ways: one is a multi-encoder decoder system which encodes MWE tags in a second encoder, and the other is a BART pre-trained transformer-based system that encodes MWE representations using special tokens. We show improvements in correcting specific types of verbal MWEs based on a modified version of a standard GEC evaluation approach.
%U https://aclanthology.org/2022.mwe-1.4
%P 9-15

Markdown (Informal)

[Improving Grammatical Error Correction for Multiword Expressions](https://aclanthology.org/2022.mwe-1.4) (Taslimipoor et al., MWE 2022)

• Improving Grammatical Error Correction for Multiword Expressions (Taslimipoor et al., MWE 2022)

ACL

• Shiva Taslimipoor, Christopher Bryant, and Zheng Yuan. 2022. Improving Grammatical Error Correction for Multiword Expressions. In Proceedings of the 18th Workshop on Multiword Expressions @LREC2022, pages 9–15, Marseille, France. European Language Resources Association.