基于不确定性损失函数和任务层级注意力机制的多任务谣言检测研究

Detecting Rumors with Uncertain Loss and Task-level Attention Mechanism

【目的】通过引入不确定性损失函数和层级注意力机制,解决多任务谣言检测研究中主观设定主任务和辅助任务问题。【方法】融合谣言勘探、立场检测和谣言检测任务的领域信息,构建改进的任务层级注意力机制模型。同时,首次在多任务谣言检测研究中,引入同方差不确定性损失函数,替代传统损失函数。最后使用PHEME数据集,将改进模型与传统多分类模型进行对比。【结果】所提模型相比于目前最优模型,在Pheme4数据集中,Macro-F值提升4.2个百分点;在Pheme5数据集中,Macro-F值提升7.6个百分点。【局限】只在Pheme数据集进行实验测试,对于其他谣言检测数据集未测试。【结论】该模型在不划分主任务和辅助任务的情况下,仍可得到理想解。

[Objective]This paper proposes a new model with the help of uncertainty loss function and task-level attention mechanism,aiming to address the issue of setting main and auxiliary tasks in rumor detection.[Methods]First,we integrated the domain knowledge of rumor exploration,stance classification,and rumor detectioin.Then,we constructed a modified model with task-level attention mechanism.Third,we used uncertainty loss function to explore the weight relationshaip of each task and obtain better detection results.Finally,we examined our model’s performance with the Pheme4 and Pheme5 datasets.[Results]Compared to the exisiting models,the Macro-F of our model increased by 4.2 and 7.6 percentage points with Pheme4 and Pheme5.[Limitations]We only examined our model with the Pheme dataset.[Conclusions]The proposed method could effective detect rumors without dividing the main and auxiliary tasks.