Source code for CCKS 2020 competition rank-1 paper: A Joint Learning Framework for the CCKS-2020 Financial Event Extraction Task.
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.
The original link to the competition is here.
Please refer to EventExtraction\README.md and EventDetection\README.md.
The data can be obtained from here.
Please unzip and place ED-datasets.zip to EventDetection\datasets, and EE-datasets.zip to EventExtraction\datasets.
The Top 1 Winner of CCKS 2020 Competition: Few-shot Cross-domain Event Extraction Competition, Chinese Information Processing Society of China. 2020.
The Technological Innovation Award of CCKS 2020 Competition: Few-shot Cross-domain Event Extraction Competition, Chinese Information Processing Society of China. 2020.
If you find this code useful, please cite our work:
@article{DBLP:journals/dint/ShengLHGYWHLX21,
author = {Jiawei Sheng and
Qian Li and
Yiming Hei and
Shu Guo and
Bowen Yu and
Lihong Wang and
Min He and
Tingwen Liu and
Hongbo Xu},
title = {A Joint Learning Framework for the {CCKS-2020} Financial Event Extraction
Task},
journal = {Data Intell.},
volume = {3},
number = {3},
pages = {444--459},
year = {2021},
url = {https://doi.org/10.1162/dint\_a\_00098},
doi = {10.1162/DINT\_A\_00098}
}
or related repo:
@inproceedings{Sheng2021:CasEE,
title = "{C}as{EE}: {A} Joint Learning Framework with Cascade Decoding for Overlapping Event Extraction",
author = "Sheng, Jiawei and
Guo, Shu and
Yu, Bowen and
Li, Qian and
Hei, Yiming and
Wang, Lihong and
Liu, Tingwen and
Xu, Hongbo",
booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-acl.14",
doi = "10.18653/v1/2021.findings-acl.14",
pages = "164--174",
}