Event Extraction by Answering (Almost) Natural Questions (2020)

Abstract: The problem of event extraction requires detecting the event trigger and extracting its corresponding arguments. Existing work in event argument extraction typically relies heavily on entity recognition as a preprocessing/concurrent step, causing the well-known problem of error propagation. To avoid this issue, we introduce a new paradigm for event extraction by formulating it as a question answering (QA) task that extracts the event arguments in an end-to-end manner. Empirical results demonstrate that our framework outperforms prior methods substantially; in addition, it is capable of extracting event arguments for roles not seen at training time (i.e., in a zero-shot learning setting).

## introduction event extraction is a long-studied and challenging task in information extraction (ie) @xcite @xcite . its goal is to extract structured information -"what is happening" and the persons/objects that are involved -from unstructured text. the task is illustrated via an example in figure 1 , which depicts an ownership transfer event (the event type), triggered by the word "sale" (the event trigger) and accompanied by its extracted arguments -text spans denoting entities that fill a set of (semantic) roles associated with the event type (e.g., buyer, seller and artifact for ownership transfer events). recent successful approaches to event extraction have benefited from dense features extracted by neural models @xcite @xcite as well as contextualized lexical representations from pretrained language models @xcite . 1 our code and question templates for the work are open sourced at https://github.com/xinyadu/eeqa for reproduction purpose. ## methodology in this section, we first provide an overview for the framework (figure 2 ), then go deeper into details of its components: question generation strategies for template creation, as well as training and inference of qa models. ## conclusion in this paper, we introduce a new paradigm for event extraction based on question answering. we investigate how the question generation strategies affect the performance of our framework on both trigger detection and argument span extraction, and find that more natural questions lead to better performance. our framework outperforms prior works on the ace 2005 benchmark, and is capable of extracting event arguments of roles not seen at training time. for future work, it would be interesting to try incorporating broader context (e.g., paragraph/document-level context @xcite @xcite in our methods to improve the accuracy of the predictions.