  The argument role in event extraction refers to the relation between an event
and an argument participating in it. Despite the great progress in event
extraction, existing studies still depend on roles pre-defined by domain
experts. These studies expose obvious weakness when extending to emerging event
types or new domains without available roles. Therefore, more attention and
effort needs to be devoted to automatically customizing argument roles. In this
paper, we define this essential but under-explored task: open-vocabulary
argument role prediction. The goal of this task is to infer a set of argument
roles for a given event type. We propose a novel unsupervised framework,
RolePred for this task. Specifically, we formulate the role prediction problem
as an in-filling task and construct prompts for a pre-trained language model to
generate candidate roles. By extracting and analyzing the candidate arguments,
the event-specific roles are further merged and selected. To standardize the
research of this task, we collect a new event extraction dataset from
WikiPpedia including 142 customized argument roles with rich semantics. On this
dataset, RolePred outperforms the existing methods by a large margin. Source
code and dataset are available on our GitHub repository:
https://github.com/yzjiao/RolePred
