  Joint event and causality extraction is a challenging yet essential task in
information retrieval and data mining. Recently, pre-trained language models
(e.g., BERT) yield state-of-the-art results and dominate in a variety of NLP
tasks. However, these models are incapable of imposing external knowledge in
domain-specific extraction. Considering the prior knowledge of frequent n-grams
that represent cause/effect events may benefit both event and causality
extraction, in this paper, we propose convolutional knowledge infusion for
frequent n-grams with different windows of length within a joint extraction
framework. Knowledge infusion during convolutional filter initialization not
only helps the model capture both intra-event (i.e., features in an event
cluster) and inter-event (i.e., associations across event clusters) features
but also boosts training convergence. Experimental results on the benchmark
datasets show that our model significantly outperforms the strong BERT+CSNN
baseline.
