Knowledge Commons of Institute of Automation,CAS
A Topic-Aware Reinforced Model for Weakly Supervised Stance Detection | |
Penghui Wei1,2; Wenji Mao1,2; Guandan Chen1,2 | |
2019-01 | |
会议名称 | The 33rd AAAI Conference on Artificial Intelligence |
会议日期 | 2019-1 |
会议地点 | Honolulu, Hawaii, USA |
出版者 | AAAI |
摘要 | Analyzing public attitudes plays an important role in opinion mining systems. Stance detection aims to determine from a text whether its author is in favor of, against, or neutral towards a given target. One challenge of this task is that a text may not explicitly express an attitude towards the target, but existing approaches utilize target content alone to build models. Moreover, although weakly supervised approaches have been proposed to ease the burden of manually annotating largescale training data, such approaches are confronted with noisy labeling problem. To address the above two issues, in this paper, we propose a Topic-Aware Reinforced Model (TARM) for weakly supervised stance detection. Our model consists of two complementary components: (1) a detection network that incorporates target-related topic information into representation learning for identifying stance effectively; (2) a policy network that learns to eliminate noisy instances from auto-labeled data based on off-policy reinforcement learning. Two networks are alternately optimized to improve each other’s performances. Experimental results demonstrate that our proposed model TARM outperforms the state-of-the-art approaches. |
收录类别 | EI |
语种 | 英语 |
七大方向——子方向分类 | 自然语言处理 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/44757 |
专题 | 多模态人工智能系统全国重点实验室_互联网大数据与信息安全 |
通讯作者 | Wenji Mao |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.University of Chinese Academy of Sciences |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Penghui Wei,Wenji Mao,Guandan Chen. A Topic-Aware Reinforced Model for Weakly Supervised Stance Detection[C]:AAAI,2019. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
[2019AAAI] A Topic-A(1184KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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