Exploiting the Ground-Truth: An Adversarial Imitation Based Knowledge Distillation Approach for Event Detection

By Jian Liu, Yubo Chen, Kang Liu. 2019

67 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Dacheng Tao
2. Jian Liu
3. Yubo Chen
4. Jianping Gou
5. Kang Liu
6. S. Maybank
7. Baosheng Yu
8. Wei Bi

In Computer Vision and Pattern Recognition

1. Dacheng Tao
2. Jianping Gou
3. S. Maybank
4. Baosheng Yu
5. Yubo Chen
6. Wei Bi
7. Jian Liu
8. Kang Liu

Citations in the corpus

• Knowledge Distillation: A Survey (2020, methodology): “Furthermore, the parameters of the teacher model (or the connections between layers) also contain another knowledge (Liu et al. 2019c).”
• Event Extraction as Machine Reading Comprehension (2020, background): “…Neural Networks (Chen et al., 2015), Recurrent Neural Networks (Nguyen et al., 2016; Sha et al., 2018), Graph Convolutional Neural Networks (Liu et al., 2018b, 2019b), and other advanced architectures (Yang and Mitchell, 2016; Liu et al., 2018a, 2019a; Nguyen and Nguyen, 2019; Zhang et al., 2019).”