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Pretrained Embeddings for Stance Detection with Hierarchical Capsule Network on Social Media
ACM Transactions on Information Systems ( IF 5.6 ) Pub Date : 2020-09-15 , DOI: 10.1145/3412362
Guangzhen Zhao 1 , Peng Yang 2
Affiliation  

Stance detection on social media aims to identify the stance of social media users toward a topic or claim, which can provide powerful information for various downstream tasks. Many existing stance detection approaches neglect to model the deep semantic representation information in tweets and do not explore aggregating the hierarchical features among words, thus degrading performance. To address these issues, this article proposes a novel deep learning approach P retrained E mbeddings for Stance Detection with H ierarchical C apsule N etwork (PE-HCN) without complicated preprocessing. Specifically, PE-HCN first adopts a pretrained language model and then uses a related textual entailment task for fine-tuning to obtain the deep textual representations of tweets. The PE-HCN approach extends the dynamic routing scheme to cope with these deep textual representations by utilizing primary capsules for routing the information among words in each tweet and applying secondary capsules to transmit the aggregated features to each category capsule accordingly. Moreover, to improve the confidences of the category capsules, we design an adaptive feedback mechanism to dynamically strengthen the routing signals. Through experiments on three benchmark datasets, compared with the state-of-the-art baselines, the extensive results exhibit that PE-HCN achieves competitive improvements of up to 6.32%, 2.09%, and 1.8%, respectively.

中文翻译:

在社交媒体上使用分层胶囊网络进行姿态检测的预训练嵌入

社交媒体上的立场检测旨在识别社交媒体用户对某个主题或主张的立场,这可以为各种下游任务提供强有力的信息。许多现有的姿态检测方法忽略了对推文中的深层语义表示信息进行建模,并且没有探索聚合单词之间的层次特征,从而降低了性能。为了解决这些问题,本文提出了一种新颖的深度学习方法再培训用于姿态检测的嵌入H分层的C胶囊ñ网络(PE-HCN),无需复杂的预处理。具体来说,PE-HCN 首先采用预训练的语言模型,然后使用相关的文本蕴涵任务进行微调,以获得推文的深度文本表示。PE-HCN 方法扩展了动态路由方案以处理这些深度文本表示,方法是利用主胶囊在每条推文中的单词之间路由信息,并应用辅助胶囊将聚合的特征相应地传输到每个类别胶囊。此外,为了提高类别胶囊的置信度,我们设计了一种自适应反馈机制来动态增强路由信号。通过在三个基准数据集上的实验,与最先进的基线相比,广泛的结果表明 PE-HCN 实现了高达 6 的竞争改进。
更新日期:2020-09-15
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