Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

11-2020

Abstract

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.

Discipline

Databases and Information Systems | Social Media

Research Areas

Data Science and Engineering

Publication

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, Virtual Conference, November 16-20

First Page

1392

Last Page

1401

Identifier

10.18653/v1/2020.emnlp-main.108

Publisher

Association for Computational Linguistics

City or Country

Virtual Conference

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