Building an Event Extractor with Only a Few Examples (2022)

Abstract: Supervised event extraction models require a substantial amount of training data to perform well. However, event annotation requires a lot of human effort and costs much time, which limits the application of existing supervised approaches to new event types. In order to reduce manual labor and shorten the time to build an event extraction system for an arbitrary event ontology, we present a new framework to train such systems much more efficiently without large annotations. Our event trigger labeling model uses a weak supervision approach, which only requires a set of keywords, a small number of examples and an unlabeled corpus, on which our approach automatically collects weakly supervised annotations. Our argument role labeling component performs zero-shot learning, which only requires the names of the argument roles of new event types. The source codes of our event trigger detection1 and event argument extraction2 models are publicly available for research purposes. We also release a dockerized system connecting the two models into an unified event extraction pipeline.

## introduction supervised event extraction models require sufficient training data to achieve a good performance. however, event annotation is a challenging task costing a lot of time and manual effort due to the sparsity of event mentions in natural language and the potentially large number of emergent event types that human annotators need to keep in mind during annotation. therefore, annotation becomes a bottleneck that slows down the development of supervised event extraction systems whenever a ⇤ these authors contributed equally to this work. 1 https://github.com/perfec-yu/ efficient-event-extraction 2 https://github.com/zhangzx-uiuc/ zero-shot-event-arguments 3 https://hub.docker.com/repository/ docker/zixuan11/event-extractor new scenario of interest emerges with new event types in need of new data. in order to meet the needs of fast development of event extraction systems for emergent new event types, we present a novel framework that can train event extraction systems with very few resources. our proposed framework includes a weakly supervised approach to train a event trigger labeling model and a zero-shot model for argument role labeling. our proposed weakly supervised event trigger labeling model only requires a few keywords and a small number of example event mentions. in our experiments on the ace 2005 english dataset, foot_0 we use 4.9 keywords and 7.3 example mentions per event type on average, which are all extracted from the ace annotation guidelines. we also propose a zero-shot argument role labeling model that only requires the argument role names of new event types to perform the task. since such information is typically included in the target ontology and annotation guidelines, we believe this required input costs much less than human annotations. our framework can be applied to any new event types. our trigger labeling component outperforms existing few-shot and zero-shot methods @xcite @xcite on ace 2005 english dataset. ## approach our framework includes two components: a trigger labeling model trained from a few keywords and example mentions per each new event type and an unlabeled corpus; and a zero-shot argument role labeling model which only needs the corresponding argument role names for extraction. ## conclusions in this work we present an efficient event extraction framework that can be trained with only a few keywords and example event mentions per new event type. we use weak supervision for trigger labeling and apply a zero-shot framework for argument role labeling. our framework can collect training data and build models for emergent new event types in a significantly shortened time without needing to acquire large-scale human annotations.