SPContrastNet:用于少量文本分类的自定进度对比学习模型

IF 5.4 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Junfan Chen, Richong Zhang, Xiaohan Jiang, Chunming Hu
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引用次数: 0

摘要

元学习(Meta-learning)近来推动了少量文本分类(few-shot text classification)的发展,它通过一系列小型任务或事件,根据从源类中传递的信息来识别目标类。在原型网络上构建元学习器的现有作品在学习相似类别之间的区别性文本表征方面需要改进,这可能会导致标签预测中的冲突。少数训练实例导致的过拟合问题也需要充分解决。此外,还应该利用高效的插集采样程序来加强少量训练。为了解决上述问题,我们首先提出了一种对比学习框架,该框架在通过无监督对比正则化减轻过拟合问题的同时,还通过有监督对比学习学习了具有区分性的文本表征,然后我们在此基础上建立了一种高效的自定步调情节采样方法,随着训练的进行,将更多的困难情节纳入其中。在 8 个少量文本分类数据集上的经验结果表明,我们的模型优于目前最先进的模型。广泛的实验分析表明,我们的有监督对比表示学习和无监督对比正则化技术提高了少量文本分类的性能。情节采样分析表明,我们的自定步调采样策略提高了训练效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SPContrastNet: A Self-Paced Contrastive Learning Model for Few-Shot Text Classification

Meta-learning has recently promoted few-shot text classification, which identifies target classes based on information transferred from source classes through a series of small tasks or episodes. Existing works constructing their meta-learner on Prototypical Networks need improvement in learning discriminative text representations between similar classes that may lead to conflicts in label prediction. The overfitting problems caused by a few training instances need to be adequately addressed. In addition, efficient episode sampling procedures that could enhance few-shot training should be utilized. To address the problems mentioned above, we first present a contrastive learning framework that simultaneously learns discriminative text representations via supervised contrastive learning while mitigating the overfitting problem via unsupervised contrastive regularization, and then we build an efficient self-paced episode sampling approach on top of it to include more difficult episodes as training progresses. Empirical results on 8 few-shot text classification datasets show that our model outperforms the current state-of-the-art models. The extensive experimental analysis demonstrates that our supervised contrastive representation learning and unsupervised contrastive regularization techniques improve the performance of few-shot text classification. The episode-sampling analysis reveals that our self-paced sampling strategy improves training efficiency.

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来源期刊
ACM Transactions on Information Systems
ACM Transactions on Information Systems 工程技术-计算机:信息系统
CiteScore
9.40
自引率
14.30%
发文量
165
审稿时长
>12 weeks
期刊介绍: The ACM Transactions on Information Systems (TOIS) publishes papers on information retrieval (such as search engines, recommender systems) that contain: new principled information retrieval models or algorithms with sound empirical validation; observational, experimental and/or theoretical studies yielding new insights into information retrieval or information seeking; accounts of applications of existing information retrieval techniques that shed light on the strengths and weaknesses of the techniques; formalization of new information retrieval or information seeking tasks and of methods for evaluating the performance on those tasks; development of content (text, image, speech, video, etc) analysis methods to support information retrieval and information seeking; development of computational models of user information preferences and interaction behaviors; creation and analysis of evaluation methodologies for information retrieval and information seeking; or surveys of existing work that propose a significant synthesis. The information retrieval scope of ACM Transactions on Information Systems (TOIS) appeals to industry practitioners for its wealth of creative ideas, and to academic researchers for its descriptions of their colleagues'' work.
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