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        <namePart type="given">Jianing</namePart>
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    <abstract>Recent advancements in joint speech-text pre-training have significantly advanced the processing of natural language. However, a key limitation is their reliance on parallel speech-text data, posing challenges due to data accessibility. Addressing this, our paper introduces an innovative framework for jointly performing speech and text processing without parallel corpora during pre-training but only downstream. Utilizing pre-trained unimodal models, we extract distinct representations for speech and text, aligning them effectively in a newly defined space using a multi-level contrastive learning mechanism. A unique swap reconstruction mechanism enhances the alignment and is followed by fusion via a multi-head mechanism, seamlessly merging modality-invariant and modality-specific representations. Testing for emotion recognition (SLU task) and idiom usage detection (NLU task) demonstrates robust performance, with commendable robustness to noise in text or speech data.</abstract>
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%0 Conference Proceedings
%T CLASP: Cross-modal Alignment Using Pre-trained Unimodal Models
%A Zhou, Jianing
%A Zeng, Ziheng
%A Gong, Hongyu
%A Bhat, Suma
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics: ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F zhou-etal-2024-clasp
%X Recent advancements in joint speech-text pre-training have significantly advanced the processing of natural language. However, a key limitation is their reliance on parallel speech-text data, posing challenges due to data accessibility. Addressing this, our paper introduces an innovative framework for jointly performing speech and text processing without parallel corpora during pre-training but only downstream. Utilizing pre-trained unimodal models, we extract distinct representations for speech and text, aligning them effectively in a newly defined space using a multi-level contrastive learning mechanism. A unique swap reconstruction mechanism enhances the alignment and is followed by fusion via a multi-head mechanism, seamlessly merging modality-invariant and modality-specific representations. Testing for emotion recognition (SLU task) and idiom usage detection (NLU task) demonstrates robust performance, with commendable robustness to noise in text or speech data.
%R 10.18653/v1/2024.findings-acl.684
%U https://aclanthology.org/2024.findings-acl.684
%U https://doi.org/10.18653/v1/2024.findings-acl.684
%P 11518-11531

Markdown (Informal)

[CLASP: Cross-modal Alignment Using Pre-trained Unimodal Models](https://aclanthology.org/2024.findings-acl.684) (Zhou et al., Findings 2024)

• CLASP: Cross-modal Alignment Using Pre-trained Unimodal Models (Zhou et al., Findings 2024)

ACL

• Jianing Zhou, Ziheng Zeng, Hongyu Gong, and Suma Bhat. 2024. CLASP: Cross-modal Alignment Using Pre-trained Unimodal Models. In Findings of the Association for Computational Linguistics: ACL 2024, pages 11518–11531, Bangkok, Thailand. Association for Computational Linguistics.