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        <title>Varying Sentence Representations via Condition-Specified Routers</title>
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    <abstract>Semantic similarity between two sentences is inherently subjective and can vary significantly based on the specific aspects emphasized. Consequently, traditional sentence encoders must be capable of generating conditioned sentence representations that account for diverse conditions or aspects. In this paper, we propose a novel yet efficient framework based on transformer-style language models that facilitates advanced conditioned sentence representation while maintaining model parameters and computational efficiency. Empirical evaluations on the Conditional Semantic Textual Similarity and Knowledge Graph Completion tasks demonstrate the superiority of our proposed framework.</abstract>
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%0 Conference Proceedings
%T Varying Sentence Representations via Condition-Specified Routers
%A Lin, Ziyong
%A Wang, Quansen
%A Jia, Zixia
%A Zheng, Zilong
%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 lin-etal-2024-varying
%X Semantic similarity between two sentences is inherently subjective and can vary significantly based on the specific aspects emphasized. Consequently, traditional sentence encoders must be capable of generating conditioned sentence representations that account for diverse conditions or aspects. In this paper, we propose a novel yet efficient framework based on transformer-style language models that facilitates advanced conditioned sentence representation while maintaining model parameters and computational efficiency. Empirical evaluations on the Conditional Semantic Textual Similarity and Knowledge Graph Completion tasks demonstrate the superiority of our proposed framework.
%U https://aclanthology.org/2024.emnlp-main.963
%P 17390-17401

Markdown (Informal)

[Varying Sentence Representations via Condition-Specified Routers](https://aclanthology.org/2024.emnlp-main.963) (Lin et al., EMNLP 2024)

• Varying Sentence Representations via Condition-Specified Routers (Lin et al., EMNLP 2024)

ACL

• Ziyong Lin, Quansen Wang, Zixia Jia, and Zilong Zheng. 2024. Varying Sentence Representations via Condition-Specified Routers. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 17390–17401, Miami, Florida, USA. Association for Computational Linguistics.