A Hybrid Detection and Generation Framework with Separate Encoders for Event Extraction (2023)

Abstract: The event extraction task typically consists of event detection and event argument extraction. Most previous work models these two subtasks with shared representation by multiple classification tasks or a unified generative approach. In this paper, we revisit this pattern and propose to use independent encoders to model event detection and event argument extraction, respectively, and use the output of event detection to construct the input of event argument extraction. In addition, we use token-level features to precisely control the fusion between two encoders to achieve joint bridging training rather than directly reusing representations between different tasks. Through a series of careful experiments, we demonstrate the importance of avoiding feature interference of different tasks and the importance of joint bridging training. We achieved competitive results on standard benchmarks (ACE05-E, ACE05-E+, and ERE-EN) and established a solid baseline.

## introduction event extraction has always been an important and challenging task in natural language processing (nlp) @xcite . it aims to extract event triggers with specific types and event arguments with correct roles from unstructured plain texts into a structured form, which mostly describes "who, when, where, what, why" and "how" of real-world events that happened @xcite . for example, figure 1 shows a meet event, triggered by "met", which describes the entity "kelly" meet with another entity "officials" in place "seoul". previous studies can be roughly classified into classification-based and generation-based methods depending on the decoder used. classificationbased method usually divides ee into two subtasks: (1) event detection (ed), which identifies event triggers and their types. (2) event argument extraction (eae) extracts the arguments and their corresponding roles for given event triggers and then models them as classification tasks, either learned in a pipeline framework or a joint formulation. recently, generation-based event extraction methods have emerged as an alternative to traditional classification-based methods due to their better data-efficient and flexibility to include additional guidance. these methods take a sentence with discrete or continuous prompts as input and use bart-style backbone learning to summarize the sentence into a natural sentence based on a manually designed template. the template is composed of natural utterances describing argument role labels, which can provide rich label semantics, leading to great success in generation-based event extraction. however, most of these methods simultaneously learn shared representations for ed and eae. as shown in previous works @xcite , ed relies more on lexical (e.g., lemma, synonyms) and shallow syntactic features (e.g., pos tags, dependent and governor words of trigger words). at the same time, the eae task focuses more on syntactic dependency features. for example, the dependency path between trigger and arguments @xcite . simply using shared representations dealing with the two distinct tasks would hurt their performance. this phenomenon is also observed in similar tasks, such as entity relation extraction @xcite , where they use two different berts for modeling entity extraction and relation extraction, respectively. to this end, we propose a simple but empirically powerful hybrid framework for event extraction. we model ed and eae using separate encoders to avoid feature interference between these two tasks. in addition, we conduct extensive experiments to investigate the difference between classificationbased and generation-based methods, and we observe that (1) classification-based methods are superior to generation-based methods in modeling token classification tasks. (2) generation-based methods perform better in modeling eae because they can capture label semantics. based on the observations, we instantiate our model with two different decoders: a classification-style decoder for ed and a generation-style decoder for eae. finally, to enhance the interaction between these two tasks, we design a bridging mechanism to provide eae with information derived from ed and a twostage training method that uses gradients from eae to guide ed learning. we evaluate our model on three widely used benchmarks, ace05-e, ace05-e + , and ere-en. experimental results show that our model establishes the new state-of-the-art on ace05-e and ace05-e + , and achieves comparable results on the ere-en dataset. our contributions can be summarized as follows: ## related work event extraction is usually considered to be composed of two sub-tasks: event detection and event argument extraction. previous researchers are keen to use a shared encoder to model the contextual representation of different tasks. we group existing event extraction methods into classification-based and generation-based. classification-based method. classificationbased methods tend to model event extraction as a classification task @xcite @xcite @xcite @xcite and deal with the recognition of trigger and arguments separately @xcite , e.g., @xcite only consider the event detection and @xcite only consider the extraction of event arguments. some previous works @xcite have tried to joint training these two tasks to enhance the connection between them, and yang and mitchell (2016 in addition, some works @xcite @xcite solve the event extraction in sequence labeling manner @xcite @xcite by tagging the sentence only once, which may not solve the overlapping problem. ## conclusion in this paper, we revisit the classification-based and generation-based event extraction methods and empirically propose a simple but robust hybrid event extraction scheme. our model learns two independent encoders for event detection and event argument extraction and uses simple trigger marker and contextual representation fusion to bridge training jointly, for which we devise a two-stage training approach. we conduct extensive analyses to understand the superior performance of our approach. these analyses verify the effectiveness of using the classification model and the generative model to learn the contextual representation of event detection and event argument extraction separately and validate the importance of taking the result of event detection as the input of event argument extraction. we hope this simple model will serve as a strong benchmark for end-to-end event extraction and make us rethink the value of a shared representation of multi-tasks.