<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="gangi-reddy-etal-2022-towards">
    <titleInfo>
        <title>Towards Robust Neural Retrieval with Source Domain Synthetic Pre-Finetuning</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Revanth</namePart>
        <namePart type="family">Gangi Reddy</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Vikas</namePart>
        <namePart type="family">Yadav</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Md</namePart>
        <namePart type="given">Arafat</namePart>
        <namePart type="family">Sultan</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Martin</namePart>
        <namePart type="family">Franz</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Vittorio</namePart>
        <namePart type="family">Castelli</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Heng</namePart>
        <namePart type="family">Ji</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Avirup</namePart>
        <namePart type="family">Sil</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2022-10</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 29th International Conference on Computational Linguistics</title>
        </titleInfo>
        <originInfo>
            <publisher>International Committee on Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Gyeongju, Republic of Korea</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Research on neural IR has so far been focused primarily on standard supervised learning settings, where it outperforms traditional term matching baselines. Many practical use cases of such models, however, may involve previously unseen target domains. In this paper, we propose to improve the out-of-domain generalization of Dense Passage Retrieval (DPR) - a popular choice for neural IR - through synthetic data augmentation only in the source domain. We empirically show that pre-finetuning DPR with additional synthetic data in its source domain (Wikipedia), which we generate using a fine-tuned sequence-to-sequence generator, can be a low-cost yet effective first step towards its generalization. Across five different test sets, our augmented model shows more robust performance than DPR in both in-domain and zero-shot out-of-domain evaluation.</abstract>
    <identifier type="citekey">gangi-reddy-etal-2022-towards</identifier>
    <location>
        <url>https://aclanthology.org/2022.coling-1.89</url>
    </location>
    <part>
        <date>2022-10</date>
        <extent unit="page">
            <start>1065</start>
            <end>1070</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Towards Robust Neural Retrieval with Source Domain Synthetic Pre-Finetuning
%A Gangi Reddy, Revanth
%A Yadav, Vikas
%A Sultan, Md Arafat
%A Franz, Martin
%A Castelli, Vittorio
%A Ji, Heng
%A Sil, Avirup
%S Proceedings of the 29th International Conference on Computational Linguistics
%D 2022
%8 October
%I International Committee on Computational Linguistics
%C Gyeongju, Republic of Korea
%F gangi-reddy-etal-2022-towards
%X Research on neural IR has so far been focused primarily on standard supervised learning settings, where it outperforms traditional term matching baselines. Many practical use cases of such models, however, may involve previously unseen target domains. In this paper, we propose to improve the out-of-domain generalization of Dense Passage Retrieval (DPR) - a popular choice for neural IR - through synthetic data augmentation only in the source domain. We empirically show that pre-finetuning DPR with additional synthetic data in its source domain (Wikipedia), which we generate using a fine-tuned sequence-to-sequence generator, can be a low-cost yet effective first step towards its generalization. Across five different test sets, our augmented model shows more robust performance than DPR in both in-domain and zero-shot out-of-domain evaluation.
%U https://aclanthology.org/2022.coling-1.89
%P 1065-1070

Markdown (Informal)

[Towards Robust Neural Retrieval with Source Domain Synthetic Pre-Finetuning](https://aclanthology.org/2022.coling-1.89) (Gangi Reddy et al., COLING 2022)

• Towards Robust Neural Retrieval with Source Domain Synthetic Pre-Finetuning (Gangi Reddy et al., COLING 2022)

ACL

• Revanth Gangi Reddy, Vikas Yadav, Md Arafat Sultan, Martin Franz, Vittorio Castelli, Heng Ji, and Avirup Sil. 2022. Towards Robust Neural Retrieval with Source Domain Synthetic Pre-Finetuning. In Proceedings of the 29th International Conference on Computational Linguistics, pages 1065–1070, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.