面向不和谐检测的迁移与主动学习：应对稀有类别挑战
Transfer and Active Learning for Dissonance Detection: Addressing the Rare-Class Challenge
AI总结：
针对不和谐检测的稀有类别数据获取难题，提出稀有类别概率（PRC）主动学习策略并研究迁移学习方案，实验表明PRC可有效提升模型准确率，有序迁移学习能改善冷启动但无助于主动学习迭代。
摘要：
尽管基于Transformer的系统已能以更少训练样本实现更高准确率，但稀有类别任务（即类别标签出现频率极低，例如占样本比例<5%）的数据获取障碍依然存在。主动学习通常被提出用于缓解这类挑战，但选择策略（即挑选稀有类别样本的标准）的选择尚未得到系统评估。此外，Transformer为迭代式迁移学习方法提供了可能。我们通过利用在密切相关任务上训练的模型，并评估多种数据获取策略（包括我们提出的稀有类别概率（PRC）方法），提出并研究了针对不和谐检测这一稀有类别问题的迁移学习与主动学习解决方案。我们针对一个具体的稀有类别问题开展了这些实验：从社交媒体收集认知不和谐的语言样本。我们发现，PRC是一种简单有效的策略，能够指导标注工作并最终提升模型准确率；而按特定顺序进行迁移学习可以改善学习器的冷启动性能，但对主动学习的迭代过程没有助益。
英文摘要：
While transformer-based systems have enabled greater accuracies with fewer training examples, data acquisition obstacles still persist for rare-class tasks -- when the class label is very infrequent (e.g. < 5% of samples). Active learning has in general been proposed to alleviate such challenges, but choice of selection strategy, the criteria by which rare-class examples are chosen, has not been systematically evaluated. Further, transformers enable iterative transfer-learning approaches. We propose and investigate transfer- and active learning solutions to the rare class problem of dissonance detection through utilizing models trained on closely related tasks and the evaluation of acquisition strategies, including a proposed probability-of-rare-class (PRC) approach. We perform these experiments for a specific rare class problem: collecting language samples of cognitive dissonance from social media. We find that PRC is a simple and effective strategy to guide annotations and ultimately improve model accuracy while transfer-learning in a specific order can improve the cold-start performance of the learner but does not benefit iterations of active learning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
