O ne EE : A One-Stage Framework for Fast Overlapping and Nested Event Extraction (2022)

Abstract: Event extraction (EE) is an essential task of information extraction, which aims to extract structured event information from unstructured text. Most prior work focuses on extracting flat events while neglecting overlapped or nested ones. A few models for overlapped and nested EE includes several successive stages to extract event triggers and arguments,which suffer from error propagation. Therefore, we design a simple yet effective tagging scheme and model to formulate EE as word-word relation recognition, called OneEE. The relations between trigger or argument words are simultaneously recognized in one stage with parallel grid tagging, thus yielding a very fast event extraction speed. The model is equipped with an adaptive event fusion module to generate event-aware representations and a distance-aware predictor to integrate relative distance information for word-word relation recognition, which are empirically demonstrated to be effective mechanisms. Experiments on 3 overlapped and nested EE benchmarks, namely FewFC, Genia11, and Genia13, show that OneEE achieves the state-of-the-art (SOTA) results. Moreover, the inference speed of OneEE is faster than those of baselines in the same condition, and can be further substantially improved since it supports parallel inference.

## introduction event extraction (ee) is a fundamental yet challenging task in information extraction research @xcite @xcite @xcite . ee facilitates the development of practical applications such as knowledge graph construction @xcite , biological process analysis @xcite , and financial market surveillance @xcite . the goal of ee is to recognize event triggers as well as the associated arguments from texts. as an example, figure 1 (a) illustrates a share reduction event including a trigger "reduced" and a subject argument "wang yawei". traditional methods for ee @xcite @xcite @xcite regard event extraction as a sequence labeling task, assuming that event mentions do not overlap with each other. however, they neglect complicated irregular ee scenarios (i.e., overlapped and nested ee) @xcite . as exemplified in figure 1(b) , there are two overlapped events, investment, and share transfer, which share the same trigger word "acquired" and the argument words "guangzhou securities". figure 1 (c) illustrates an example of nested events where the event gene ## problem formulation the goal of event extraction includes extracting event triggers and their arguments. we can formalize overlapping and nested ee as follows: given an input sentence consisting of n tokens or words @xmath0, x 2 , . . . , x n } and event type @xmath1, the task aims to extract the span relations s and the role relations r between each token pair (x i , x j ), where e denotes the event type collection, s and r are pre-defined tags. these relations can be explained below, and we also give an example as demonstrated in figure 2 for better understanding. ## framework the architecture of our model is illustrated in figure 3 , which mainly consists of three components. first, the widely-used pre-trained language model, bert @xcite , is used as the encoder to yield contextualized word representations from the input sentences. then, an adaptive event fusion layer consisting of an attention module and two gate modules is used to integrate the target event type embedding into contextual representations. afterward, a prediction layer is employed to jointly extract the span relations and the role relations between word pairs. ## conclusion in this paper, we propose a novel one-stage framework based on word-word relation recognition to address overlapped and nested ee concurrently. the relations between word pairs are pre-defined as the word-word relations within a trigger or argument and cross a trigger-argument pair. moreover, we propose an efficient model that consists of an adaptive event fusion layer for integrating the target event representation, and a distance-aware prediction layer for identifying all kinds of relations jointly. experimental results show that our proposed model achieves new sota results on three datasets and faster speed than the sota model. through ablation studies, we find that the adaptive event fusion layer and distance-aware prediction layer are effective in improving the model performance. in future work, we will extend our method to other structured prediction tasks, such as structured sentiment analysis and overlapped entity relation extraction.