@inproceedings{liu-etal-2026-uncertainty,
title = "Uncertainty-Aware Contrastive Sentence Embedding With Local Context Representation for Text Classification",
author = "Liu, Han and
Zhan, Jiaqing and
Chen, Zhichao and
Zhang, Qin",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1927/",
pages = "38702--38716",
ISBN = "979-8-89176-395-1",
abstract = "In real-world applications of natural language processing, it is essential to effectively adapt a pre-trained model to a downstream task. While text classification is undertaken as a downstream task, it is crucial to produce meaningful sentence embedding that is adaptive to the task. In this paper, we explore how to effectively adapt a pre-trained model for extracting meaningful context representations from sentences, and propose an uncertainty-aware contrastive sentence embedding approach that involves addressing language ambiguity and inter-class separability for a text classification task. Specifically, we design an end-to-end strategy for driving the process of learning to transform a word embedding matrix into a contextualized sentence vector and to quantify the representation uncertainty of the sentence, while the word embedding matrix is produced by a pre-trained model without fine-tuning, and a label-wise contrastive learning strategy is designed to enhance intra-class compactness and inter-class separability. The results on public data sets show that a considerable improvement of text classification accuracy is achieved by adopting the proposed approach in comparison with using those state-of-the-art methods."
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%0 Conference Proceedings
%T Uncertainty-Aware Contrastive Sentence Embedding With Local Context Representation for Text Classification
%A Liu, Han
%A Zhan, Jiaqing
%A Chen, Zhichao
%A Zhang, Qin
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F liu-etal-2026-uncertainty
%X In real-world applications of natural language processing, it is essential to effectively adapt a pre-trained model to a downstream task. While text classification is undertaken as a downstream task, it is crucial to produce meaningful sentence embedding that is adaptive to the task. In this paper, we explore how to effectively adapt a pre-trained model for extracting meaningful context representations from sentences, and propose an uncertainty-aware contrastive sentence embedding approach that involves addressing language ambiguity and inter-class separability for a text classification task. Specifically, we design an end-to-end strategy for driving the process of learning to transform a word embedding matrix into a contextualized sentence vector and to quantify the representation uncertainty of the sentence, while the word embedding matrix is produced by a pre-trained model without fine-tuning, and a label-wise contrastive learning strategy is designed to enhance intra-class compactness and inter-class separability. The results on public data sets show that a considerable improvement of text classification accuracy is achieved by adopting the proposed approach in comparison with using those state-of-the-art methods.
%U https://aclanthology.org/2026.findings-acl.1927/
%P 38702-38716
Markdown (Informal)
[Uncertainty-Aware Contrastive Sentence Embedding With Local Context Representation for Text Classification](https://aclanthology.org/2026.findings-acl.1927/) (Liu et al., Findings 2026)
ACL