Dynamic Prefix-Tuning for Generative Template-based Event Extraction (2022)

Abstract: We consider event extraction in a generative manner with template-based conditional generation. Although there is a rising trend of casting the task of event extraction as a sequence generation problem with prompts, these generation-based methods have two significant challenges, including using suboptimal prompts and static event type information. In this paper, we propose a generative template-based event extraction method with dynamic prefix (GTEE-DynPref) by integrating context information with type-specific prefixes to learn a context-specific prefix for each context. Experimental results show that our model achieves competitive results with the state-of-the-art classification-based model OneIE on ACE 2005 and achieves the best performances on ERE.Additionally, our model is proven to be portable to new types of events effectively.

## introduction event extraction is an essential yet challenging task for natural language understanding. given a piece of text, event extraction systems need to recognize event triggers with specific types and the event arguments with the correct roles in each event record according to an event ontology, which defines the event types and argument roles @xcite . as an example, the context in figure 1 contains two event records, a transport event triggered by "returned" and an arrest-jail event triggered by "capture". in the transport event, the artifact is "the man", the destination is "los angeles" and the origin is "mexico". in the arrest-jail event, the person is "the man", the time is "tuesday" and the agent is "bounty hunters". in this work, we focus on the task setting of extracting * corresponding author. most of the event extraction work treats the extraction of event triggers and event arguments as several classification tasks, either learned in a pipelined framework @xcite @xcite or a joint formulation @xcite @xcite @xcite . there is a rising trend of casting the task of event extraction as a sequence generation problem by applying special decoding strategies @xcite or steering pretrained language models to output conditional generation sequences with discrete prompts @xcite . compared with classification-based methods, this line of work is more data-efficient and flexible, which requires less annotated data to achieve acceptable model performances, being easier to extend to new event types by slightly modifying the designed prompts and decoding strategies. however, these generation-based methods have two significant challenges, which impede achieving competitive results with the classification-based methods. (1) suboptimal prompts: first, they manually design prompts for each event type @xcite , which are suboptimal without tuning and largely affect the model performances. (2) static event type information: second, when extracting events of a particular type, recent generation-based methods will receive the same event type information concerning only the running event type, regardless of the associations between other possible event types. to alleviate the above two challenges, we propose a generative template-based event extraction method with dynamic prefixes, denoted as gtee-dynpref. as demonstrated in figure 1 , we follow our method generation-based method generation-based event extraction 1) extracting transport event 2) extracting arrest-jail event trigger person time agent capture the man tuesday bounty hunters event type arrest-jail event type transport trigger artifact destination origin returned the man los angeles mexico ## related work this paper is related to the following lines of work. ## generative template-based method we revisit the task of event extraction as the process of conditional generation and present our base model (gtee-base) as illustrated in figure 2 . ## dynamic prefix-tuning we propose dynamic prefix-tuning with taskspecific prefix and context-specific prefix to alleviate the two main challenges in generation-based event extraction. the framework of our model with dynamic prefix tuning, gtee-dynpref, is shown in figure 3 . we will introduce the dynamic prefix-tuning step by step. ## conclusion in this paper, we studied event extraction in the template-based conditional generation manner. we proposed the dynamic prefix tuning model gtee-dynpref for event extraction. on the one hand the method constructs tunable prefixes to model type-specific information and on the other hand gtee-dynpref captures the associations between event types and calculates a context-specific prefix when steering pretrained language models. experimental results show that our model achieves competitive results with the state-of-the-art on ace 2005, which is also proven to be portable to new types of events effectively.