Before revision:  In particular, the paper makes the followingcontributions : (1) it  uses BERT embeddings to define contextual word and contextual sentence embeddings as attributes, which to the best of our knowledge were never used before for the ED task; (2) the proposed model has the ability to use its first layer to learn good feature representations; ( 3) a new public dataset with a general definition of event;
After revision:  In particular, the paper makes the followingcontributions : (1) it uses BERT embeddings to define contextual word and contextual sentence embeddings as attributes, which to the best of our knowledge were never used before for detecting ongoing events in news; ( 3) a new public dataset with a general definition of event;