DEGREE : A Data-Efficient Generation-Based Event Extraction Model (2022)

Abstract: Event extraction requires high-quality expert human annotations, which are usually expensive. Therefore, learning a data-efficient event extraction model that can be trained with only a few labeled examples has become a crucial challenge. In this paper, we focus on low-resource end-to-end event extraction and propose DEGREE, a data-efficient model that formulates event extraction as a conditional generation problem. Given a passage and a manually designed prompt, DEGREE learns to summarize the events mentioned in the passage into a natural sentence that follows a predefined pattern. The final event predictions are then extracted from the generated sentence with a deterministic algorithm. DEGREE has three advantages to learn well with less training data. First, our designed prompts provide semantic guidance for DEGREE to leverage DEGREE and thus better capture the event arguments. Moreover, DEGREE is capable of using additional weakly-supervised information, such as the description of events encoded in the prompts. Finally, DEGREE learns triggers and arguments jointly in an end-to-end manner, which encourages the model to better utilize the shared knowledge and dependencies among them. Our experimental results demonstrate the strong performance of DEGREE for low-resource event extraction.

## introduction event extraction (ee) aims to extract events, each of which consists of a trigger and several participants (arguments) with their specific roles, from a given passage. for example, in figure 1 , a justice:execute event is triggered by the word "execution" and this event contains three argument roles, including an agent (indonesia) who carries out the execution, a person who is executed (convicts), and a place where the event occurs (not mentioned in the passage). previous work @xcite justice:appeal plaintiff : five prosecutor : none adjudicator : supreme court place : indonesia figure 1 : two examples of events (justice:execute and justice:appeal) extracted from the given passage. @xcite usually divides ee into two subtasks: (1) event detection, which identifies event triggers and their types, and (2) event argument extraction, which extracts the arguments and their roles for given event triggers. ee has been shown to benefit a wide range of applications, e.g., building knowledge graphs @xcite , question answering @xcite , and other downstream studies @xcite @xcite . most prior works on ee rely on a large amount of annotated data for training @xcite @xcite @xcite @xcite . however, high-quality event annotations are expensive to obtain. for example, the ace 2005 corpus @xcite , one of the most widely used ee datasets, requires two rounds of annotations by linguistics experts. the high annotation costs make these models hard to be extended to new domains and new event types. therefore, how to learn a data-efficient ee model trained with only a few annotated examples is a crucial challenge. in this paper, we focus on low-resource event extraction, where only a small amount of training examples are available for training. we propose degree (data-efficient generation-based event extraction), a generation-based model that takes a passage and a manually designed prompt as the input, and learns to summarize the passage into a ## data-efficient event extraction we introduce degree, a generation-based model for low-resource event extraction. unlike previous works @xcite , which separate event extraction into two pipelined tasks (event detection and event argument extraction), degree is designed for the end-to-end event extraction and predict event triggers and arguments at the same time. ## experiments we conduct experiments for low-resource event extraction to study how degree performs. ## ablation studies in this section, we present comprehensive ablation studies to justify our design. to better understand the contribution of each component in the designed prompt and their effects on the different tasks, we ablate degree(eae) and degree(ed) for both low-resource and high-resource situations. any one of event type definition, query trigger, and eae template leads to performance drops, which validates their necessity. we observe that query trigger plays the most important role among the three and when less training data is given, the superiority of leveraging any of these weaklysupervised signal becomes more obvious. ## related work fully supervised event extraction. event extraction has been studied for over a decade @xcite and most traditional event extraction works follow the fully supervised setting @xcite @xcite @xcite @xcite . many of them use classification-based models and use pipeline-style frameworks to extract events @xcite @xcite . to better leverage shared knowledge in event triggers and arguments, some works propose incorporating global features to jointly decide triggers and arguments @xcite @xcite . recently, few generation-based event extraction models have been proposed @xcite @xcite . tanl @xcite treats event extraction as translation tasks between augmented natural languages. their predicted target-augmented language embed labels into the input passage via using brackets and vertical bar symbols. tempgen @xcite is a template-based role-filler entity extraction model, which generate outputs that fill role entities into non-natural templated sequences. the output sequence designs of tanl and temp-gen hinder the models from fully leveraging label semantics, unlike degree that generates natural sentences. bart-gen @xcite is also a generation-based model focusing on documentlevel event argument extraction. they solve event extraction with a pipeline, which prevents knowledge sharing across subtasks. all these fully supervised methods can achieve substantial performance with a large amount of annotated data. however, their designs are not specific for low-resource scenarios, hence, these models can not enjoy all the benefits that degree obtains for low-resource event extraction at the same time, as we mentioned in section 1. ## conclusion & future work in this paper, we present degree, a data-efficient generation-based event extraction model. degree requires less training data because it better utilizes label semantics as well as weakly-supervised information, and captures better dependencies by jointly predicting triggers and arguments. our experimental results and ablation studies show the superiority of degree for low-resource event extraction. degree assumes that some weakly-supervised information (the description of events, similar keywords, and human-written templates) is accessible or not expensive for the users to craft. this assumption may holds for most situations. we leave the automation of template construction for future work, which can further ease the needed efforts when deploying degree in a large-scale corpus.