<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="kim-kim-2022-saving">
    <titleInfo>
        <title>Saving Dense Retriever from Shortcut Dependency in Conversational Search</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Sungdong</namePart>
        <namePart type="family">Kim</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Gangwoo</namePart>
        <namePart type="family">Kim</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2022-12</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Yoav</namePart>
            <namePart type="family">Goldberg</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Zornitsa</namePart>
            <namePart type="family">Kozareva</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Yue</namePart>
            <namePart type="family">Zhang</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Abu Dhabi, United Arab Emirates</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Conversational search (CS) needs a holistic understanding of conversational inputs to retrieve relevant passages. In this paper, we demonstrate the existence of a retrieval shortcut in CS, which causes models to retrieve passages solely relying on partial history while disregarding the latest question. With in-depth analysis, we first show that naively trained dense retrievers heavily exploit the shortcut and hence perform poorly when asked to answer history-independent questions. To build more robust models against shortcut dependency, we explore various hard negative mining strategies. Experimental results show that training with the model-based hard negatives effectively mitigates the dependency on the shortcut, significantly improving dense retrievers on recent CS benchmarks. In particular, our retriever outperforms the previous state-of-the-art model by 11.0 in Recall@10 on QReCC.</abstract>
    <identifier type="citekey">kim-kim-2022-saving</identifier>
    <identifier type="doi">10.18653/v1/2022.emnlp-main.701</identifier>
    <location>
        <url>https://aclanthology.org/2022.emnlp-main.701</url>
    </location>
    <part>
        <date>2022-12</date>
        <extent unit="page">
            <start>10278</start>
            <end>10287</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Saving Dense Retriever from Shortcut Dependency in Conversational Search
%A Kim, Sungdong
%A Kim, Gangwoo
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F kim-kim-2022-saving
%X Conversational search (CS) needs a holistic understanding of conversational inputs to retrieve relevant passages. In this paper, we demonstrate the existence of a retrieval shortcut in CS, which causes models to retrieve passages solely relying on partial history while disregarding the latest question. With in-depth analysis, we first show that naively trained dense retrievers heavily exploit the shortcut and hence perform poorly when asked to answer history-independent questions. To build more robust models against shortcut dependency, we explore various hard negative mining strategies. Experimental results show that training with the model-based hard negatives effectively mitigates the dependency on the shortcut, significantly improving dense retrievers on recent CS benchmarks. In particular, our retriever outperforms the previous state-of-the-art model by 11.0 in Recall@10 on QReCC.
%R 10.18653/v1/2022.emnlp-main.701
%U https://aclanthology.org/2022.emnlp-main.701
%U https://doi.org/10.18653/v1/2022.emnlp-main.701
%P 10278-10287

Markdown (Informal)

[Saving Dense Retriever from Shortcut Dependency in Conversational Search](https://aclanthology.org/2022.emnlp-main.701) (Kim & Kim, EMNLP 2022)

• Saving Dense Retriever from Shortcut Dependency in Conversational Search (Kim & Kim, EMNLP 2022)

ACL

• Sungdong Kim and Gangwoo Kim. 2022. Saving Dense Retriever from Shortcut Dependency in Conversational Search. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 10278–10287, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.