计算机科学
推论
自回归模型
图形
事件(粒子物理)
解码方法
光学(聚焦)
论证(复杂分析)
理论计算机科学
机器学习
数据挖掘
人工智能
算法
生物化学
化学
物理
量子力学
光学
经济
计量经济学
作者
Spyros Angelopoulos,Shahin Kamali,Kimia Shadkami
标识
DOI:10.24963/ijcai.2022/632
摘要
Most previous studies of document-level event extraction mainly focus on building argument chains in an autoregressive way, which achieves a certain success but is inefficient in both training and inference. In contrast to the previous studies, we propose a fast and lightweight model named as PTPCG. In our model, we design a novel strategy for event argument combination together with a non-autoregressive decoding algorithm via pruned complete graphs, which are constructed under the guidance of the automatically selected pseudo triggers. Compared to the previous systems, our system achieves competitive results with 19.8% of parameters and much lower resource consumption, taking only 3.8% GPU hours for training and up to 8.5 times faster for inference. Besides, our model shows superior compatibility for the datasets with (or without) triggers and the pseudo triggers can be the supplements for annotated triggers to make further improvements. Codes are available at https://github.com/Spico197/DocEE .
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