Efficient Document-level Event Extraction via Pseudo-Trigger-aware Pruned Complete Graph
This work addresses efficiency issues in event extraction for NLP researchers, offering a more resource-friendly approach with incremental improvements in speed and parameter reduction.
The paper tackles the inefficiency of autoregressive methods in document-level event extraction by proposing PTPCG, a fast and lightweight model that uses pseudo-trigger-aware pruned complete graphs and non-autoregressive decoding, achieving competitive results with only 19.8% of parameters, 3.8% GPU training hours, and up to 8.5 times faster inference.
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 .