<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="yimam-biemann-2018-par4sim">
    <titleInfo>
        <title>Par4Sim – Adaptive Paraphrasing for Text Simplification</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Seid</namePart>
        <namePart type="given">Muhie</namePart>
        <namePart type="family">Yimam</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Chris</namePart>
        <namePart type="family">Biemann</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2018-08</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 27th International Conference on Computational Linguistics</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Emily</namePart>
            <namePart type="given">M</namePart>
            <namePart type="family">Bender</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Leon</namePart>
            <namePart type="family">Derczynski</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Pierre</namePart>
            <namePart type="family">Isabelle</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Santa Fe, New Mexico, USA</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Learning from a real-world data stream and continuously updating the model without explicit supervision is a new challenge for NLP applications with machine learning components. In this work, we have developed an adaptive learning system for text simplification, which improves the underlying learning-to-rank model from usage data, i.e. how users have employed the system for the task of simplification. Our experimental result shows that, over a period of time, the performance of the embedded paraphrase ranking model increases steadily improving from a score of 62.88% up to 75.70% based on the NDCG@10 evaluation metrics. To our knowledge, this is the first study where an NLP component is adaptively improved through usage.</abstract>
    <identifier type="citekey">yimam-biemann-2018-par4sim</identifier>
    <location>
        <url>https://aclanthology.org/C18-1028</url>
    </location>
    <part>
        <date>2018-08</date>
        <extent unit="page">
            <start>331</start>
            <end>342</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Par4Sim – Adaptive Paraphrasing for Text Simplification
%A Yimam, Seid Muhie
%A Biemann, Chris
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F yimam-biemann-2018-par4sim
%X Learning from a real-world data stream and continuously updating the model without explicit supervision is a new challenge for NLP applications with machine learning components. In this work, we have developed an adaptive learning system for text simplification, which improves the underlying learning-to-rank model from usage data, i.e. how users have employed the system for the task of simplification. Our experimental result shows that, over a period of time, the performance of the embedded paraphrase ranking model increases steadily improving from a score of 62.88% up to 75.70% based on the NDCG@10 evaluation metrics. To our knowledge, this is the first study where an NLP component is adaptively improved through usage.
%U https://aclanthology.org/C18-1028
%P 331-342

Markdown (Informal)

[Par4Sim – Adaptive Paraphrasing for Text Simplification](https://aclanthology.org/C18-1028) (Yimam & Biemann, COLING 2018)

• Par4Sim – Adaptive Paraphrasing for Text Simplification (Yimam & Biemann, COLING 2018)

ACL

• Seid Muhie Yimam and Chris Biemann. 2018. Par4Sim – Adaptive Paraphrasing for Text Simplification. In Proceedings of the 27th International Conference on Computational Linguistics, pages 331–342, Santa Fe, New Mexico, USA. Association for Computational Linguistics.