文本回归预训练模型的不确定性估计与减少

IF 4.2 1区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Yuxia Wang, Daniel Beck, Timothy Baldwin, K. Verspoor
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引用次数: 13

摘要

目前最先进的分类和回归模型往往没有很好地校准,不能可靠地提供不确定性估计,限制了它们在临床决策等安全关键应用中的效用。虽然最近的工作主要集中在分类器的校准上,但在回归设置中几乎没有关于NLP校准的工作。在本文中,我们量化了文本回归的预训练语言模型的校准,包括内在的和外在的。我们进一步应用不确定性估计来增加低资源领域的训练数据。我们在自我训练和主动学习设置下的三个回归任务上的实验表明,不确定性估计可以用来提高整体性能和增强模型泛化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Uncertainty Estimation and Reduction of Pre-trained Models for Text Regression
Abstract State-of-the-art classification and regression models are often not well calibrated, and cannot reliably provide uncertainty estimates, limiting their utility in safety-critical applications such as clinical decision-making. While recent work has focused on calibration of classifiers, there is almost no work in NLP on calibration in a regression setting. In this paper, we quantify the calibration of pre- trained language models for text regression, both intrinsically and extrinsically. We further apply uncertainty estimates to augment training data in low-resource domains. Our experiments on three regression tasks in both self-training and active-learning settings show that uncertainty estimation can be used to increase overall performance and enhance model generalization.
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来源期刊
CiteScore
32.60
自引率
4.60%
发文量
58
审稿时长
8 weeks
期刊介绍: The highly regarded quarterly journal Computational Linguistics has a companion journal called Transactions of the Association for Computational Linguistics. This open access journal publishes articles in all areas of natural language processing and is an important resource for academic and industry computational linguists, natural language processing experts, artificial intelligence and machine learning investigators, cognitive scientists, speech specialists, as well as linguists and philosophers. The journal disseminates work of vital relevance to these professionals on an annual basis.
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