CASIA OpenIR  > 模式识别国家重点实验室  > 自然语言处理
Exploiting the Ground-Truth: An Adversarial Imitation Based Knowledge Distillation Approach for Event Detection
Jian Liu; Chen, Yubo; Liu, Kang
2019
Conference NameAmerican Association for AI National Conference(AAAI 2019)
Conference DateJanuary 27 - February 1.
Conference PlaceHawaii, USA
Abstract

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.

Indexed ByEI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/26129
Collection模式识别国家重点实验室_自然语言处理
Affiliation中国科学院自动化研究所
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Jian Liu,Chen, Yubo,Liu, Kang. Exploiting the Ground-Truth: An Adversarial Imitation Based Knowledge Distillation Approach for Event Detection[C],2019.
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