摘要: This paper presents a winning solution for the CCKS-2020 financial event extraction task, where the goal
is to identify event types, triggers and arguments in sentences across multiple event types. In this task, we
focus on resolving two challenging problems (i.e., low resources and element overlapping) by proposing a
joint learning framework, named SaltyFishes. We first formulate the event extraction task as a joint probability
model. By sharing parameters in the model across different types, we can learn to adapt to low-resource
events based on high-resource events. We further address the element overlapping problems by a mechanism
of Conditional Layer Normalization, achieving even better extraction accuracy. The overall approach achieves
an F1-score of 87.8% which ranks the first place in the competition.
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期刊:
DATA INTELLIGENCE
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分类:
计算机科学
>>
计算机科学的集成理论
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引用:
ChinaXiv:202211.00386
(或此版本
ChinaXiv:202211.00386V1)
DOI:10.1162/dint_a_00098
CSTR:32003.36.ChinaXiv.202211.00386.V1
- 推荐引用方式:
Jiawei, Sheng,Qian, Li,Yiming, Hei,Shu, Guo,Bowen, Yu,Lihong, Wang,Min, He,Tingwen, Liu,Hongbo, Xu.(2022).A Joint Learning Framework for the CCKS-2020 Financial Event Extraction Task.数据智能(英文).doi:10.1162/dint_a_00098
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