Event Extraction as Question Generation and Answering (2023)

Abstract: Recent work on Event Extraction has reframed the task as Question Answering (QA), with promising results. The advantage of this approach is that it addresses the error propagation issue found in traditional token-based classification approaches by directly predicting event arguments without extracting candidates first. However, the questions are typically based on fixed templates and they rarely leverage contextual information such as relevant arguments. In addition, prior QA-based approaches have difficulty handling cases where there are multiple arguments for the same role. In this paper, we propose QGA-EE, which enables a Question Generation (QG) model to generate questions that incorporate rich contextual information instead of using fixed templates. We also propose dynamic templates to assist the training of QG model. Experiments show that QGA-EE outperforms all prior single-task-based models on the ACE05 English dataset.

## introduction event extraction (ee) aims to extract core information elements (e.g. who, what, where, when) from text, and is a very important task in natural language processing @xcite . it provides inputs to downstream applications such as summarization @xcite , knowledge base population @xcite , and recommendation @xcite . previous work @xcite @xcite is typically based on a pipeline approach, which first identifies the event trigger word/phrase and argument candidates, and then applies a classifier to the pair-wise features to classify the roles of the candidates. unfortunately, errors tend to propagate down the pipeline. recently, some approaches have formulated ee as a question answering (qa) problem @xcite @xcite to mitigate the issue, in which questions for each argument role are manually defined by templates. for example, extracting the attack argument from the conflict.attack event in the sentence 'that's because coalition fighter jets pummeled this iraqi position on the hills above chamchamal and iraqi troops made a hasty retreat.' is reframed as answering the question 'who was the attacking agent?' these approaches have shown promising results, but template-based questions are limiting: since the templates are built manually, they are fixed and rarely include contextual information (i.e., specific to the inputs), except for trigger words in some work @xcite . formulating good questions, however, has been shown to improve performance for standard qa tasks @xcite . for qa-based ee, a question that incorporates richer contextual information such as other event arguments could yield better results (e.g. 'who used jets in the attack in hills?' in figure 1 ). in this paper, we propose qga-ee, which consists of 1) a qg model for generating a contextaware question conditioned on a target argument role and 2) a qa model for answering the contextaware question to extract the event argument. we also design dynamic templates to generate the gold context-aware questions for qg model training. to the best of our knowledge, this is the first qa-based ee work that utilizes dynamic templates and focuses on generating context-aware questions. li et al. (2020) also propose a model to generate questions that incorporate contextual information for both event trigger and arguments. however, our work has two main advantages. first, in @xcite the question only incorporates the contextual information at the ontology level (e.g. argument role, event type). in our work, the generated questions incorporate contextual information at an event mention-level. for example, the question generated by our model includes the real event argument rather than just the argument role (e.g. 'hills' vs 'place'). second, the questions in their work are generated by filling in the templates, but our templates are dynamic and used to train the qg model which can automatically generate the optimal question given a specific event mention and the concerned argument role. experimental results show that qga-ee outperforms all of the single-task-based models on the automatic content extraction (ace) 2005 english dataset @xcite and even achieves competitive performance with state-of-the-art joint ie models. ## model figure 1 shows the overall framework of qga-ee. it focuses on event argument extraction (eae) only, but can be paired with any event trigger tagger to perform end-to-end ee. in section 4, we pair it with a standard sequence labeling trigger tagger to evaluate its end-to-end ee performance. ## conclusion in this paper, we present qga-ee, a novel sequence-to-sequence based framework for ee, which utilizes a qg model to generate contextaware questions as inputs to a qa model for eae. our model naturally supports the cases in which multiple event arguments play the same role within a specific event mention. we conduct experiments on the ace05-e dataset and the proposed model outperforms all of the single-task-based models and achieves competitive results with state-of-theart joint ie models. in the future, we plan to utilize the extensibility of the qa framework to incorporate knowledge from semi-structured eventrelevant data such as wikipedia infoboxes. we also plan to extend our approach to multilingual ee and joint ie.