Abstract
Previous studies have shown that the multi-task learning paradigm with the stance classification could facilitate the successful detection of rumours, but the shared layers in multi-task learning tend to yield a compromise between the general and the task-specific representation of structural information. To address this issue, we propose a novel Multi-Task Learning framework with Shared Multi-channel Interactions (MTL-SMI), which is composed of two shared channels and two task-specific graph channels. The shared channels extract task-invariant text features and structural features, and the task-specific graph channels, by interacting with the shared channels, extract the task-enhanced structural features. Experiments on two realworld datasets show the superiority of MTL-SMI against strong baselines.
Y. Liu and X. Yang—Equal contribution.
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Acknowledgements
This work was supported by the Natural Science Foundation of China (No. 61976026, No. 61902394) and 111 Project (B18008).
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Liu, Y., Yang, X., Zhang, X., Tang, Z., Chen, Z., Liwen, Z. (2022). Predicting Rumor Veracity on Social Media with Graph Structured Multi-task Learning. In: Bhattacharya, A., et al. Database Systems for Advanced Applications. DASFAA 2022. Lecture Notes in Computer Science, vol 13247. Springer, Cham. https://doi.org/10.1007/978-3-031-00129-1_16
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