  Capturing interactions among event arguments is an essential step towards
robust event argument extraction (EAE). However, existing efforts in this
direction suffer from two limitations: 1) The argument role type information of
contextual entities is mainly utilized as training signals, ignoring the
potential merits of directly adopting it as semantically rich input features;
2) The argument-level sequential semantics, which implies the overall
distribution pattern of argument roles over an event mention, is not well
characterized. To tackle the above two bottlenecks, we formalize EAE as a
Seq2Seq-like learning problem for the first time, where a sentence with a
specific event trigger is mapped to a sequence of event argument roles. A
neural architecture with a novel Bi-directional Entity-level Recurrent Decoder
(BERD) is proposed to generate argument roles by incorporating contextual
entities' argument role predictions, like a word-by-word text generation
process, thereby distinguishing implicit argument distribution patterns within
an event more accurately.
