  Document-level event argument extraction poses new challenges of long input
and cross-sentence inference compared to its sentence-level counterpart.
However, most prior works focus on capturing the relations between candidate
arguments and the event trigger in each event, ignoring two crucial points: a)
non-argument contextual clue information; b) the relevance among argument
roles. In this paper, we propose a SCPRG (Span-trigger-based Contextual Pooling
and latent Role Guidance) model, which contains two novel and effective modules
for the above problem. The Span-Trigger-based Contextual Pooling(STCP)
adaptively selects and aggregates the information of non-argument clue words
based on the context attention weights of specific argument-trigger pairs from
pre-trained model. The Role-based Latent Information Guidance (RLIG) module
constructs latent role representations, makes them interact through
role-interactive encoding to capture semantic relevance, and merges them into
candidate arguments. Both STCP and RLIG introduce no more than 1% new
parameters compared with the base model and can be easily applied to other
event extraction models, which are compact and transplantable. Experiments on
two public datasets show that our SCPRG outperforms previous state-of-the-art
methods, with 1.13 F1 and 2.64 F1 improvements on RAMS and WikiEvents
respectively. Further analyses illustrate the interpretability of our model.
