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 .Comment: Accepted to IJCAI'202