<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="yang-etal-2023-longtriever">
    <titleInfo>
        <title>Longtriever: a Pre-trained Long Text Encoder for Dense Document Retrieval</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Junhan</namePart>
        <namePart type="family">Yang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Zheng</namePart>
        <namePart type="family">Liu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Chaozhuo</namePart>
        <namePart type="family">Li</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Guangzhong</namePart>
        <namePart type="family">Sun</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Xing</namePart>
        <namePart type="family">Xie</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>Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing</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>Pre-trained language models (PLMs) have achieved the preeminent position in dense retrieval due to their powerful capacity in modeling intrinsic semantics. However, most existing PLM-based retrieval models encounter substantial computational costs and are infeasible for processing long documents. In this paper, a novel retrieval model Longtriever is proposed to embrace three core challenges of long document retrieval: substantial computational cost, incomprehensive document understanding, and scarce annotations. Longtriever splits long documents into short blocks and then efficiently models the local semantics within a block and the global context semantics across blocks in a tightly-coupled manner. A pre-training phase is further proposed to empower Longtriever to achieve a better understanding of underlying semantic correlations. Experimental results on two popular benchmark datasets demonstrate the superiority of our proposal.</abstract>
    <identifier type="citekey">yang-etal-2023-longtriever</identifier>
    <identifier type="doi">10.18653/v1/2023.emnlp-main.223</identifier>
    <location>
        <url>https://aclanthology.org/2023.emnlp-main.223</url>
    </location>
    <part>
        <date>2023-12</date>
        <extent unit="page">
            <start>3655</start>
            <end>3665</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Longtriever: a Pre-trained Long Text Encoder for Dense Document Retrieval
%A Yang, Junhan
%A Liu, Zheng
%A Li, Chaozhuo
%A Sun, Guangzhong
%A Xie, Xing
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F yang-etal-2023-longtriever
%X Pre-trained language models (PLMs) have achieved the preeminent position in dense retrieval due to their powerful capacity in modeling intrinsic semantics. However, most existing PLM-based retrieval models encounter substantial computational costs and are infeasible for processing long documents. In this paper, a novel retrieval model Longtriever is proposed to embrace three core challenges of long document retrieval: substantial computational cost, incomprehensive document understanding, and scarce annotations. Longtriever splits long documents into short blocks and then efficiently models the local semantics within a block and the global context semantics across blocks in a tightly-coupled manner. A pre-training phase is further proposed to empower Longtriever to achieve a better understanding of underlying semantic correlations. Experimental results on two popular benchmark datasets demonstrate the superiority of our proposal.
%R 10.18653/v1/2023.emnlp-main.223
%U https://aclanthology.org/2023.emnlp-main.223
%U https://doi.org/10.18653/v1/2023.emnlp-main.223
%P 3655-3665

Markdown (Informal)

[Longtriever: a Pre-trained Long Text Encoder for Dense Document Retrieval](https://aclanthology.org/2023.emnlp-main.223) (Yang et al., EMNLP 2023)

• Longtriever: a Pre-trained Long Text Encoder for Dense Document Retrieval (Yang et al., EMNLP 2023)

ACL

• Junhan Yang, Zheng Liu, Chaozhuo Li, Guangzhong Sun, and Xing Xie. 2023. Longtriever: a Pre-trained Long Text Encoder for Dense Document Retrieval. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 3655–3665, Singapore. Association for Computational Linguistics.