基于改进元学习的交叉目标姿态检测

Huishan Ji, Zheng Lin, Peng Fu, Weiping Wang
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引用次数: 3

摘要

跨目标立场检测(CTSD)旨在识别文本对目标的立场,其中立场注释可用于(尽管相关但)不同的目标。近年来,人们提出了基于外部语义和情感知识的CTSD模型,并取得了良好的效果。然而,这样的解决方案依赖于很多外部资源,并且只利用一个源目标,这是对其他可用目标的浪费。为了解决上述问题,我们提出了一种基于元学习的多对一CTSD模型。为了充分发挥元学习的作用,我们使用一种平衡的、简单难学的学习模式来进一步完善它。具体来说,对于多目标训练,我们根据目标之间的相似度来馈送模型,并利用两种重新平衡策略来处理数据的不平衡。我们在SemEval 2016 task 6上进行了实验,结果表明我们的方法是有效的,并为CTSD建立了一个新的最先进的宏观f1分数。
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Cross-Target Stance Detection Via Refined Meta-Learning
Cross-target stance detection (CTSD) aims to identify the stance of the text towards a target, where stance annotations are available for (though related but) different targets. Recently, models based on external semantic and emotion knowledge have been proposed for CTSD, achieving promising performance. However, such solutions rely on much external resources and harness only one source target, which is a waste of other available targets. To address the problem above, we propose a many-to-one CTSD model based on meta-learning. To make the most of meta-learning, we further refine it with a balanced and easy-to-hard learning pattern. Specifically, for multiple-target training, we feed the model according to the similarity among targets, and utilize two kinds of re-balanced strategies to deal with the imbalance in data. We conduct experiments on SemEval 2016 task 6, and results demonstrate that our method is effective and establishes a new state-of-the-art macro-f1 score for CTSD.
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