Knowledge Commons of Institute of Automation,CAS
Exploiting the Ground-Truth: An Adversarial Imitation Based Knowledge Distillation Approach for Event Detection | |
Liu, Jian1,2![]() ![]() ![]() | |
2019-02 | |
会议名称 | AAAI |
页码 | 6754-6761 |
会议日期 | 2019-02 |
会议地点 | Honolulu, Hawaii |
摘要 | The ambiguity in language expressions poses a great challenge for event detection. To disambiguate event types, current approaches rely on external NLP toolkits to build knowledge representations. Unfortunately, these approaches work in a pipeline paradigm and suffer from error propagation problem. In this paper, we propose an adversarial imitation based knowledge distillation approach, for the first time, to tackle the challenge of acquiring knowledge from rawsentences for event detection. In our approach, a teacher module is first devised to learn the knowledge representations from the ground-truth annotations. Then, we set up a student module that only takes the raw-sentences as the input. The student module is taught to imitate the behavior of the teacher under the guidance of an adversarial discriminator. By this way, the process of knowledge distillation from rawsentence has been implicitly integrated into the feature encoding stage of the student module. To the end, the enhanced student is used for event detection, which processes raw texts and requires no extra toolkits, naturally eliminating the error propagation problem faced by pipeline approaches. We conduct extensive experiments on the ACE 2005 datasets, and the experimental results justify the effectiveness of our approach. |
收录类别 | EI |
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/173211/39208 |
专题 | 多模态人工智能系统全国重点实验室_自然语言处理 |
作者单位 | 1.中国科学院自动化研究所 2.中国科学院大学 |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Liu, Jian,Chen, Yubo,Liu, Kang. Exploiting the Ground-Truth: An Adversarial Imitation Based Knowledge Distillation Approach for Event Detection[C],2019:6754-6761. |
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