<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="zihan-etal-2023-learning">
    <titleInfo>
        <title>Learning on Structured Documents for Conditional Question Answering</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Wang</namePart>
        <namePart type="family">Zihan</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Qian</namePart>
        <namePart type="family">Hongjin</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Dou</namePart>
        <namePart type="family">Zhicheng</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2023-08</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <language>
        <languageTerm type="text">English</languageTerm>
        <languageTerm type="code" authority="iso639-2b">eng</languageTerm>
    </language>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 22nd Chinese National Conference on Computational Linguistics</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Maosong</namePart>
            <namePart type="family">Sun</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Bing</namePart>
            <namePart type="family">Qin</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Xipeng</namePart>
            <namePart type="family">Qiu</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Jing</namePart>
            <namePart type="family">Jiang</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Xianpei</namePart>
            <namePart type="family">Han</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Chinese Information Processing Society of China</publisher>
            <place>
                <placeTerm type="text">Harbin, China</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>“Conditional question answering (CQA) is an important task in natural language processing thatinvolves answering questions that depend on specific conditions. CQA is crucial for domainsthat require the provision of personalized advice or making context-dependent analyses, such aslegal consulting and medical diagnosis. However, existing CQA models struggle with generatingmultiple conditional answers due to two main challenges: (1) the lack of supervised training datawith diverse conditions and corresponding answers, and (2) the difficulty to output in a complexformat that involves multiple conditions and answers. To address the challenge of limited super-vision, we propose LSD (Learning on Structured Documents), a self-supervised learning methodon structured documents for CQA. LSD involves a conditional problem generation method anda contrastive learning objective. The model is trained with LSD on massive unlabeled structureddocuments and is fine-tuned on labeled CQA dataset afterwards. To overcome the limitation ofoutputting answers with complex formats in CQA, we propose a pipeline that enables the gen-eration of multiple answers and conditions. Experimental results on the ConditionalQA datasetdemonstrate that LSD outperforms previous CQA models in terms of accuracy both in providinganswers and conditions.”</abstract>
    <identifier type="citekey">zihan-etal-2023-learning</identifier>
    <location>
        <url>https://aclanthology.org/2023.ccl-1.51</url>
    </location>
    <part>
        <date>2023-08</date>
        <extent unit="page">
            <start>583</start>
            <end>599</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Learning on Structured Documents for Conditional Question Answering
%A Zihan, Wang
%A Hongjin, Qian
%A Zhicheng, Dou
%Y Sun, Maosong
%Y Qin, Bing
%Y Qiu, Xipeng
%Y Jiang, Jing
%Y Han, Xianpei
%S Proceedings of the 22nd Chinese National Conference on Computational Linguistics
%D 2023
%8 August
%I Chinese Information Processing Society of China
%C Harbin, China
%G English
%F zihan-etal-2023-learning
%X “Conditional question answering (CQA) is an important task in natural language processing thatinvolves answering questions that depend on specific conditions. CQA is crucial for domainsthat require the provision of personalized advice or making context-dependent analyses, such aslegal consulting and medical diagnosis. However, existing CQA models struggle with generatingmultiple conditional answers due to two main challenges: (1) the lack of supervised training datawith diverse conditions and corresponding answers, and (2) the difficulty to output in a complexformat that involves multiple conditions and answers. To address the challenge of limited super-vision, we propose LSD (Learning on Structured Documents), a self-supervised learning methodon structured documents for CQA. LSD involves a conditional problem generation method anda contrastive learning objective. The model is trained with LSD on massive unlabeled structureddocuments and is fine-tuned on labeled CQA dataset afterwards. To overcome the limitation ofoutputting answers with complex formats in CQA, we propose a pipeline that enables the gen-eration of multiple answers and conditions. Experimental results on the ConditionalQA datasetdemonstrate that LSD outperforms previous CQA models in terms of accuracy both in providinganswers and conditions.”
%U https://aclanthology.org/2023.ccl-1.51
%P 583-599

Markdown (Informal)

[Learning on Structured Documents for Conditional Question Answering](https://aclanthology.org/2023.ccl-1.51) (Zihan et al., CCL 2023)

• Learning on Structured Documents for Conditional Question Answering (Zihan et al., CCL 2023)

ACL

• Wang Zihan, Qian Hongjin, and Dou Zhicheng. 2023. Learning on Structured Documents for Conditional Question Answering. In Proceedings of the 22nd Chinese National Conference on Computational Linguistics, pages 583–599, Harbin, China. Chinese Information Processing Society of China.