A Topic-Aware Reinforced Model for Weakly Supervised Stance Detection | |
Penghui Wei1,2; Wenji Mao1,2; Guandan Chen1,2 | |
2019-01 | |
会议日期 | 2019-1 |
会议地点 | Honolulu, Hawaii, USA |
英文摘要 | 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. |
会议录出版者 | AAAI |
语种 | 英语 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/44757] |
专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_互联网大数据与安全信息学研究中心 |
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 |
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]. 见:. Honolulu, Hawaii, USA. 2019-1. |
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