-The scarcity in annotated data poses a great challenge for event detection (ED). Cross-lingual ED aims to tackle this challenge by transferring knowledge between different languages to boost performance. However, previous cross-lingual methods for ED demonstrated a heavy dependency on parallel resources, which might limit their applicability. In this paper, we propose a new method for cross-lingual ED, demonstrating a minimal dependency on parallel resources. Specifically, to construct a lexical mapping between different languages, we devise a context-dependent translation method; to treat the word order difference problem, we propose a shared syntactic order event detector for multilingual co-training. The efficiency of our method is studied through extensive experiments on two standard datasets. Empirical results indicate that our method is effective in 1) performing cross-lingual transfer concerning different directions and 2) tackling the extremely annotation-poor scenario. + Meta-Learning with Dynamic-Memory-Based Prototypical Network for Few-Shot Event Detection, WSDM 2020(Github)
++Shumin Deng, Ningyu Zhang, Jiaojian Kang, Yichi Zhang, Wei Zhang, Huajun Chen
++ Exploiting the Matching Information in the Support Set for Few Shot Event Classification, PAKDD 2020(Github) + ++Viet Lai, Franck Dernoncourt, Thien Huu Nguyen +
++ Towards Few-Shot Event Mention Retrieval : An Evaluation Framework and A Siamese Network Approach, 2020(Github) + +#### 2018 ++ +
++ Zero-Shot Transfer Learning for Event Extraction, ACL 2018(Github) + + + ### 中文事件抽取 @@ -266,8 +287,49 @@ The scarcity in annotated data poses a great challenge for event detection (ED). #### 2019+Lifu Huang, Heng Ji, Kyunghyun Cho, Ido Dagan, Sebastian Riedel, Clare R. Voss +
- Neural Cross-Lingual Event Detection with Minimal Parallel Resources, EMNLP2019(Github) -The scarcity in annotated data poses a great challenge for event detection (ED). Cross-lingual ED aims to tackle this challenge by transferring knowledge between different languages to boost performance. However, previous cross-lingual methods for ED demonstrated a heavy dependency on parallel resources, which might limit their applicability. In this paper, we propose a new method for cross-lingual ED, demonstrating a minimal dependency on parallel resources. Specifically, to construct a lexical mapping between different languages, we devise a context-dependent translation method; to treat the word order difference problem, we propose a shared syntactic order event detector for multilingual co-training. The efficiency of our method is studied through extensive experiments on two standard datasets. Empirical results indicate that our method is effective in 1) performing cross-lingual transfer concerning different directions and 2) tackling the extremely annotation-poor scenario. + Cross-lingual Structure Transfer for Relation and Event Extraction, EMNLP2019(Github)
+ + ++Ananya Subburathinam, Di Lu, Heng Ji, Jonathan May, Shih-Fu Chang, Avirup Sil, Clare Voss +
++ A Hybrid Character Representatin for Chinese Event Detection, IJCNLP 2019(Github) + + ++Xi Xiangyu ; Zhang Tong ; Ye Wei ; Zhang Jinglei ; Xie Rui ; Zhang Shikun +
++ Event Detection with Trigger-Aware Lattice Neural Network, EMNLP 2019(Github) + ++Ning Ding, Ziran Li, Zhiyuan Liu, Haitao Zheng, Zibo Lin +
++ Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction, EMNLP 2019(Github) + + +#### 2018 ++Shun Zheng, Wei Cao, Wei Xu, Jiang Bian +
++ DCFFE: A Document-level Chinese Financial Event Extraction System based on Automatically Labelled Training Data, ACL 2018(Github) + ++Yang, Hang and Chen, Yubo and Liu, Kang and Xiao, Yang and Zhao, Jun +
++ Nugget Proposal Networks for Chinese Event Detection, ACL 2018(Github) + +#### 2016 ++Lin, Hongyu and Lu, Yaojie and Han, Xianpei and Sun, Le +
++ A convolution bilstm neural network model for chinese event extraction,NLPCC 2016(Github) +