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数据分析与知识发现  2021, Vol. 5 Issue (7): 101-110     https://doi.org/10.11925/infotech.2096-3467.2020.1216
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
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*
杨晗迅,周德群,马静(),罗永聪
南京航空航天大学经济与管理学院 南京 211100
Detecting Rumors with Uncertain Loss and Task-level Attention Mechanism
Yang Hanxun,Zhou Dequn,Ma Jing(),Luo Yongcong
College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, China
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摘要 

【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。

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杨晗迅
周德群
马静
罗永聪
关键词 不确定性损失函数多任务学习谣言检测注意力机制    
Abstract

[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.

Key wordsUncertain Loss    Multi-task Learning    Rumor Detection    Attention Mechanism
收稿日期: 2020-12-06      出版日期: 2021-04-09
ZTFLH:  TP393  
基金资助:*南京航空航天大学前瞻性发展策略研究基金项目(NW2020001);国家社会科学基金重点项目(20ZDA092);南京航空航天大学研究生创新基地(实验室)开放基金项目(kfjj20200901)
通讯作者: 马静,ORCID: 0000-0001-8472-2518     E-mail: majing5525@126.com
引用本文:   
杨晗迅, 周德群, 马静, 罗永聪. 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*[J]. 数据分析与知识发现, 2021, 5(7): 101-110.
Yang Hanxun, Zhou Dequn, Ma Jing, Luo Yongcong. Detecting Rumors with Uncertain Loss and Task-level Attention Mechanism. Data Analysis and Knowledge Discovery, 2021, 5(7): 101-110.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2020.1216      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2021/V5/I7/101
Fig.1  谣言识别流程
Fig.2  多任务模型
Fig.3  Branch_LSTM模型
Fig.4  基于任务层次注意力机制图
事件名称 事件文本量 回复文本量 疑似谣言 非疑似谣言 谣言 非谣言 无法判定
Charlie Hebdo 2 079 38 268 458 1621 193 116 149
Sydney Siege 1 221 23 996 522 699 382 86 54
Ferguson 1 143 24 175 284 859 10 8 266
Ottawa Shooting 890 12 284 470 420 329 72 69
Germanwings-crash 469 4 489 238 231 94 111 33
Putin Missing 238 835 126 112 0 9 117
Prince Toronto 233 902 229 4 0 222 7
Gurlitt 138 179 61 77 59 0 2
Elbola Essien 14 226 14 0 0 14 0
合计 6 425 105 354 2 402 4 023 1 067 638 697
Table 1  PHEME数据集
Fig.5  具有三个分支的谣言文本与评论的树状结构
操作系统配置 参数或版本
CPU Xeon(R) Gold 5218 CPU
GPU NVIDIA T4(16GB)
Python 3.6
TensorFlow 1.1.31
Keras 2.3.1
内存 1TB
Table 2  实验环境
实验 算法 acc Macro-F
实验一 Majority(True) 0.591 0.247
NileTMRG* 0.444 0.205
Branch-LSTM 0.466 0.362
MTL3 0.462 0.322
ES-ATT-MTL3 0.395 0.263
Task-ATT-MTL3 0.494 0.333
Un-Task-ATT-MTL3 0.425 0.364
实验二 Majority(True) 0.511 0.226
NileTMRG* 0.438 0.339
Branch-LSTM 0.454 0.336
MTL3 0.492 0.396
ES-ATT-MTL3 0.459 0.280
Task-ATT-MTL3 0.505 0.372
Un-Task-ATT-MTL3 0.467 0.472
实验三 Majority(True) 0.444 0.205
NileTMRG* 0.360 0.297
Branch-LSTM 0.314 0.259
MTL3 0.405 0.405
ES-ATT-MTL3 0.356 0.240
Task-ATT-MTL3 0.418 0.347
Un-Task-ATT-MTL3 0.385 0.393
Table 3  谣言检测任务实验结果
事件 acc Macro-F 谣言类
平均
F1
非谣言类平均F1 无法确定类平均F1
Charlie Hebdo 0.292 0.213 0.147 0.131 0.362
Sydney Siege 0.339 0.204 0.400 0.162 0.501
Ferguson 0.697 0.268 0 0.004 0.801
Ottawa Shooting 0.675 0.306 0.806 0.107 0.010
Germanwings-crash 0.356 0.245 0.310 0.355 0.091
Table 4  单事件结果
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