<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="xiao-etal-2023-topic">
    <titleInfo>
        <title>Topic-DPR: Topic-based Prompts for Dense Passage Retrieval</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Qingfa</namePart>
        <namePart type="family">Xiao</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Shuangyin</namePart>
        <namePart type="family">Li</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Lei</namePart>
        <namePart type="family">Chen</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2023-12</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Findings of the Association for Computational Linguistics: EMNLP 2023</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Houda</namePart>
            <namePart type="family">Bouamor</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Juan</namePart>
            <namePart type="family">Pino</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Kalika</namePart>
            <namePart type="family">Bali</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Singapore</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Prompt-based learning’s efficacy across numerous natural language processing tasks has led to its integration into dense passage retrieval. Prior research has mainly focused on enhancing the semantic understanding of pre-trained language models by optimizing a single vector as a continuous prompt. This approach, however, leads to a semantic space collapse; identical semantic information seeps into all representations, causing their distributions to converge in a restricted region. This hinders differentiation between relevant and irrelevant passages during dense retrieval. To tackle this issue, we present Topic-DPR, a dense passage retrieval model that uses topic-based prompts. Unlike the single prompt method, multiple topic-based prompts are established over a probabilistic simplex and optimized simultaneously through contrastive learning. This encourages representations to align with their topic distributions, improving space uniformity. Furthermore, we introduce a novel positive and negative sampling strategy, leveraging semi-structured data to boost dense retrieval efficiency. Experimental results from two datasets affirm that our method surpasses previous state-of-the-art retrieval techniques.</abstract>
    <identifier type="citekey">xiao-etal-2023-topic</identifier>
    <identifier type="doi">10.18653/v1/2023.findings-emnlp.480</identifier>
    <location>
        <url>https://aclanthology.org/2023.findings-emnlp.480</url>
    </location>
    <part>
        <date>2023-12</date>
        <extent unit="page">
            <start>7216</start>
            <end>7225</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Topic-DPR: Topic-based Prompts for Dense Passage Retrieval
%A Xiao, Qingfa
%A Li, Shuangyin
%A Chen, Lei
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F xiao-etal-2023-topic
%X Prompt-based learning’s efficacy across numerous natural language processing tasks has led to its integration into dense passage retrieval. Prior research has mainly focused on enhancing the semantic understanding of pre-trained language models by optimizing a single vector as a continuous prompt. This approach, however, leads to a semantic space collapse; identical semantic information seeps into all representations, causing their distributions to converge in a restricted region. This hinders differentiation between relevant and irrelevant passages during dense retrieval. To tackle this issue, we present Topic-DPR, a dense passage retrieval model that uses topic-based prompts. Unlike the single prompt method, multiple topic-based prompts are established over a probabilistic simplex and optimized simultaneously through contrastive learning. This encourages representations to align with their topic distributions, improving space uniformity. Furthermore, we introduce a novel positive and negative sampling strategy, leveraging semi-structured data to boost dense retrieval efficiency. Experimental results from two datasets affirm that our method surpasses previous state-of-the-art retrieval techniques.
%R 10.18653/v1/2023.findings-emnlp.480
%U https://aclanthology.org/2023.findings-emnlp.480
%U https://doi.org/10.18653/v1/2023.findings-emnlp.480
%P 7216-7225

Markdown (Informal)

[Topic-DPR: Topic-based Prompts for Dense Passage Retrieval](https://aclanthology.org/2023.findings-emnlp.480) (Xiao et al., Findings 2023)

• Topic-DPR: Topic-based Prompts for Dense Passage Retrieval (Xiao et al., Findings 2023)

ACL

• Qingfa Xiao, Shuangyin Li, and Lei Chen. 2023. Topic-DPR: Topic-based Prompts for Dense Passage Retrieval. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 7216–7225, Singapore. Association for Computational Linguistics.