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        <title>Condenser: a Pre-training Architecture for Dense Retrieval</title>
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        <namePart type="given">Luyu</namePart>
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            <title>Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing</title>
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        <name type="personal">
            <namePart type="given">Marie-Francine</namePart>
            <namePart type="family">Moens</namePart>
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        <name type="personal">
            <namePart type="given">Xuanjing</namePart>
            <namePart type="family">Huang</namePart>
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        <name type="personal">
            <namePart type="given">Lucia</namePart>
            <namePart type="family">Specia</namePart>
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    <abstract>Pre-trained Transformer language models (LM) have become go-to text representation encoders. Prior research fine-tunes deep LMs to encode text sequences such as sentences and passages into single dense vector representations for efficient text comparison and retrieval. However, dense encoders require a lot of data and sophisticated techniques to effectively train and suffer in low data situations. This paper finds a key reason is that standard LMs’ internal attention structure is not ready-to-use for dense encoders, which needs to aggregate text information into the dense representation. We propose to pre-train towards dense encoder with a novel Transformer architecture, Condenser, where LM prediction CONditions on DENSE Representation. Our experiments show Condenser improves over standard LM by large margins on various text retrieval and similarity tasks.</abstract>
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    <identifier type="doi">10.18653/v1/2021.emnlp-main.75</identifier>
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%0 Conference Proceedings
%T Condenser: a Pre-training Architecture for Dense Retrieval
%A Gao, Luyu
%A Callan, Jamie
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F gao-callan-2021-condenser
%X Pre-trained Transformer language models (LM) have become go-to text representation encoders. Prior research fine-tunes deep LMs to encode text sequences such as sentences and passages into single dense vector representations for efficient text comparison and retrieval. However, dense encoders require a lot of data and sophisticated techniques to effectively train and suffer in low data situations. This paper finds a key reason is that standard LMs’ internal attention structure is not ready-to-use for dense encoders, which needs to aggregate text information into the dense representation. We propose to pre-train towards dense encoder with a novel Transformer architecture, Condenser, where LM prediction CONditions on DENSE Representation. Our experiments show Condenser improves over standard LM by large margins on various text retrieval and similarity tasks.
%R 10.18653/v1/2021.emnlp-main.75
%U https://aclanthology.org/2021.emnlp-main.75
%U https://doi.org/10.18653/v1/2021.emnlp-main.75
%P 981-993

Markdown (Informal)

[Condenser: a Pre-training Architecture for Dense Retrieval](https://aclanthology.org/2021.emnlp-main.75) (Gao & Callan, EMNLP 2021)

• Condenser: a Pre-training Architecture for Dense Retrieval (Gao & Callan, EMNLP 2021)

ACL

• Luyu Gao and Jamie Callan. 2021. Condenser: a Pre-training Architecture for Dense Retrieval. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 981–993, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.