More recently, BERT, a deep bidirectional representation which jointly conditions on both left and right context (Devlin et al., 2019), was proposed, which unlike MUSE, provides contextualized word embeddings, and has been shown to achieve state-of-the-art performance on many NLP tasks.
(Yang et al., 2019) propose a method based on BERT for enhancing event trigger and argument extraction by generating more labeled data.