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        <title>Language Concept Erasure for Language-invariant Dense Retrieval</title>
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        <namePart type="given">Zhiqi</namePart>
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        <namePart type="given">Puxuan</namePart>
        <namePart type="family">Yu</namePart>
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    <name type="personal">
        <namePart type="given">Shauli</namePart>
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    <name type="personal">
        <namePart type="given">James</namePart>
        <namePart type="family">Allan</namePart>
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            <title>Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Yaser</namePart>
            <namePart type="family">Al-Onaizan</namePart>
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            <namePart type="given">Mohit</namePart>
            <namePart type="family">Bansal</namePart>
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            <namePart type="given">Yun-Nung</namePart>
            <namePart type="family">Chen</namePart>
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    <abstract>Multilingual models aim for language-invariant representations but still prominently encode language identity. This, along with the scarcity of high-quality parallel retrieval data, limits their performance in retrieval. We introduce LANCER, a multi-task learning framework that improves language-invariant dense retrieval by reducing language-specific signals in the embedding space. Leveraging the notion of linear concept erasure, we design a loss function that penalizes cross-correlation between representations and their language labels. LANCER leverages only English retrieval data and general multilingual corpora, training models to focus on language-invariant retrieval by semantic similarity without necessitating a vast parallel corpus. Experimental results on various datasets show our method consistently improves over baselines, with extensive analyses demonstrating greater language agnosticism.</abstract>
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%0 Conference Proceedings
%T Language Concept Erasure for Language-invariant Dense Retrieval
%A Huang, Zhiqi
%A Yu, Puxuan
%A Ravfogel, Shauli
%A Allan, James
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F huang-etal-2024-language
%X Multilingual models aim for language-invariant representations but still prominently encode language identity. This, along with the scarcity of high-quality parallel retrieval data, limits their performance in retrieval. We introduce LANCER, a multi-task learning framework that improves language-invariant dense retrieval by reducing language-specific signals in the embedding space. Leveraging the notion of linear concept erasure, we design a loss function that penalizes cross-correlation between representations and their language labels. LANCER leverages only English retrieval data and general multilingual corpora, training models to focus on language-invariant retrieval by semantic similarity without necessitating a vast parallel corpus. Experimental results on various datasets show our method consistently improves over baselines, with extensive analyses demonstrating greater language agnosticism.
%U https://aclanthology.org/2024.emnlp-main.736
%P 13261-13273

Markdown (Informal)

[Language Concept Erasure for Language-invariant Dense Retrieval](https://aclanthology.org/2024.emnlp-main.736) (Huang et al., EMNLP 2024)

• Language Concept Erasure for Language-invariant Dense Retrieval (Huang et al., EMNLP 2024)

ACL

• Zhiqi Huang, Puxuan Yu, Shauli Ravfogel, and James Allan. 2024. Language Concept Erasure for Language-invariant Dense Retrieval. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 13261–13273, Miami, Florida, USA. Association for Computational Linguistics.