To produce more general representations, we also keep the temporal label and mask the event tokens instead at a certain probability, so that we are able to maximize both P (Tmp-Label|Event) and P (Event|Tmp-Label) in the same learning process, where Tmp-Label refers to the temporal label associated with the event.
we use the reserved "unused" tokens in BERT-base model lexicon to construct a 1-to-1 mapping from every value in every dimension to the new vocabulary.