Dynamic Prefix-Tuning for Generative Template-based Event Extraction

  title={Dynamic Prefix-Tuning for Generative Template-based Event Extraction},
  author={Xiao Liu and Heyan Huang and Ge Shi and Bo Wang},
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… 


DEGREE: A Data-Efficient Generative Event Extraction Model
This work presents a data-efficient event extraction method by formulating event extraction as a natural language generation problem and achieves superior performance over strong baselines on EE tasks in the low data regime and achieves competitive results to the current state of theart when more data becomes available.
Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction
Experimental results show that, by uniformly modeling all tasks in a single model and universally predicting different labels, the Text2Event method can achieve competitive performance using only record-level annotations in both supervised learning and transfer learning settings.
Exploring Pre-trained Language Models for Event Extraction and Generation
This work proposes an event extraction model to overcome the roles overlap problem by separating the argument prediction in terms of roles, and proposes a method to automatically generate labeled data by editing prototypes and screen out generated samples by ranking the quality.
Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation
This paper proposes a novel Jointly Multiple Events Extraction framework to jointly extract multiple event triggers and arguments by introducing syntactic shortcut arcs to enhance information flow and attention-based graph convolution networks to model graph information.
Joint Event Extraction via Structured Prediction with Global Features
This work proposes a joint framework based on structured prediction which extracts triggers and arguments together so that the local predictions can be mutually improved, and proposes to incorporate global features which explicitly capture the dependencies of multiple triggers and argued.
Event Extraction by Answering (Almost) Natural Questions
This work introduces a new paradigm for event extraction by formulating it as a question answering (QA) task, which extracts the event arguments in an end-to-end manner and outperforms prior methods substantially.
Document-Level Event Argument Extraction by Conditional Generation
A document-level neural event argument extraction model is proposed by formulating the task as conditional generation following event templates by creating the first end-to-end zero-shot event extraction framework.
Event Extraction as Multi-turn Question Answering
This work proposes a new paradigm that formulates event extraction as multi-turn question answering, MQAEE, which makes full use of dependency among arguments and event types, and generalizes well to new types with new argument roles.
Event Extraction as Machine Reading Comprehension
This paper proposes a new learning paradigm of EE, by explicitly casting it as a machine reading comprehension problem (MRC), which includes an unsupervised question generation process, which can transfer event schema into a set of natural questions, followed by a BERT-based question-answering process to retrieve answers as EE results.
Entity, Relation, and Event Extraction with Contextualized Span Representations
This work examines the capabilities of a unified, multi-task framework for three information extraction tasks: named entity recognition, relation extraction, and event extraction (called DyGIE++) and achieves state-of-the-art results across all tasks.