Given the weights α, we build a consolidated representation of context event chain e 1 , e 2 , ..., e n−1 as a weighted sum of h 1 , h 2 , ..., h n−1 : The event candidate h c and the new representation of the existing chain h e can be further integrated to deduce a deeper representation of the full event chain hypothesis to the next layer (hop 2), denoted as v. v contains deeper semantic information compared with h c , which encode the temporal order of the event chain [h 1 , h 2 , ..., h n−1 , h c ] without differentiating the weights of each event.
in the next hop, better event weights can potentially be deduced by using v instead of h c in the calculation of attention: In the same way, we stack multiple hops and repeat the steps multiple times, so that more abstract evidences can be extracted according to the chain of existing events.