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        <title>Query-aware Multi-modal based Ranking Relevance in Video Search</title>
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    <name type="personal">
        <namePart type="given">Chengcan</namePart>
        <namePart type="family">Ye</namePart>
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    <name type="personal">
        <namePart type="given">Ting</namePart>
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    <name type="personal">
        <namePart type="given">Zhiyi</namePart>
        <namePart type="family">Zhou</namePart>
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    <name type="personal">
        <namePart type="given">Feng</namePart>
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        <dateIssued>2023-12</dateIssued>
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            <title>Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track</title>
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            <namePart type="given">Mingxuan</namePart>
            <namePart type="family">Wang</namePart>
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            <namePart type="given">Imed</namePart>
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    <abstract>Relevance ranking system plays a crucial role in video search on streaming platforms. Most relevance ranking methods focus on text modality, incapable of fully exploiting cross-modal cues present in video. Recent multi-modal models have demonstrated promise in various vision-language tasks but provide limited help for downstream query-video relevance tasks due to the discrepency between relevance ranking-agnostic pre-training objectives and the real video search scenarios that demand comprehensive relevance modeling. To address these challenges, we propose a QUery-Aware pre-training model with multi-modaLITY (QUALITY) that incorporates hard-mined query information as alignment targets and utilizes video tag information for guidance. QUALITY is integrated into our relevance ranking model, which leverages multi-modal knowledge and improves ranking optimization method based on ordinal regression. Extensive experiments show our proposed model significantly enhances video search performance.</abstract>
    <identifier type="citekey">ye-etal-2023-query</identifier>
    <identifier type="doi">10.18653/v1/2023.emnlp-industry.31</identifier>
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        <date>2023-12</date>
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            <start>322</start>
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%0 Conference Proceedings
%T Query-aware Multi-modal based Ranking Relevance in Video Search
%A Ye, Chengcan
%A Peng, Ting
%A Chang, Tim
%A Zhou, Zhiyi
%A Wang, Feng
%Y Wang, Mingxuan
%Y Zitouni, Imed
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F ye-etal-2023-query
%X Relevance ranking system plays a crucial role in video search on streaming platforms. Most relevance ranking methods focus on text modality, incapable of fully exploiting cross-modal cues present in video. Recent multi-modal models have demonstrated promise in various vision-language tasks but provide limited help for downstream query-video relevance tasks due to the discrepency between relevance ranking-agnostic pre-training objectives and the real video search scenarios that demand comprehensive relevance modeling. To address these challenges, we propose a QUery-Aware pre-training model with multi-modaLITY (QUALITY) that incorporates hard-mined query information as alignment targets and utilizes video tag information for guidance. QUALITY is integrated into our relevance ranking model, which leverages multi-modal knowledge and improves ranking optimization method based on ordinal regression. Extensive experiments show our proposed model significantly enhances video search performance.
%R 10.18653/v1/2023.emnlp-industry.31
%U https://aclanthology.org/2023.emnlp-industry.31
%U https://doi.org/10.18653/v1/2023.emnlp-industry.31
%P 322-330

Markdown (Informal)

[Query-aware Multi-modal based Ranking Relevance in Video Search](https://aclanthology.org/2023.emnlp-industry.31) (Ye et al., EMNLP 2023)

• Query-aware Multi-modal based Ranking Relevance in Video Search (Ye et al., EMNLP 2023)

ACL

• Chengcan Ye, Ting Peng, Tim Chang, Zhiyi Zhou, and Feng Wang. 2023. Query-aware Multi-modal based Ranking Relevance in Video Search. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 322–330, Singapore. Association for Computational Linguistics.