Papers by Quanxiu Wang

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Challenge:	Existing efforts to capture event argument interactions are limited by the argument role type information of contextual entities.
Approach:	They propose to capture event argument interactions as a Seq2Seq-like learning problem where a sentence with a specific event trigger is mapped to a sequence of event argument roles.
Outcome:	The proposed neural architecture generates 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.