Coupled hierarchical transformer for stance-aware rumor verification in social media conversations

• Main Authors: YU, Jianfei, JIANG, Jing, KHOO, Ling Min Serena, CHIEU, Hai Leong, XIA, Rui
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: Databases and Information Systems; Social Media
• Online Access: https://ink.library.smu.edu.sg/sis_research/5603; https://ink.library.smu.edu.sg/context/sis_research/article/6606/viewcontent/EMNLP_2020b.pdf
• Institution: Singapore Management University

The prevalent use of social media enables rapid spread of rumors on a massive scale, which leads to the emerging need of automatic rumor verification (RV). A number of previous studies focus on leveraging stance classification to enhance RV with multi-task learning (MTL) methods. However, most of these methods failed to employ pre-trained contextualized embeddings such as BERT, and did not exploit inter-task dependencies by using predicted stance labels to improve the RV task. Therefore, in this paper, to extend BERT to obtain thread representations, we first propose a Hierarchical Transformer1 , which divides each long thread into shorter subthreads, and employs BERT to separately represent each subthread, followed by a global Transformer layer to encode all the subthreads. We further propose a Coupled Transformer Module to capture the inter-task interactions and a Post-Level Attention layer to use the predicted stance labels for RV, respectively. Experiments on two benchmark datasets show the superiority of our Coupled Hierarchical Transformer model over existing MTL approaches.