# DEGREE: A Data-Efficient Generation-Based Event Extraction Model

arXiv: https://arxiv.org/abs/2108.12724 · HTML: https://mlsanity.com/paper/2108.12724/

## Key facts

- Cited by 83 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 44 references that are in the map.
- First posted on arXiv 29 Aug 2021.

## Builds on (most cited first, 19 of 19)

- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [Entity, Relation, and Event Extraction with Contextualized Span Representations](https://mlsanity.com/paper/1909.03546/) (2019)
- [Structured Prediction as Translation between Augmented Natural Languages](https://mlsanity.com/paper/2101.05779/) (2021)
- [Event Extraction by Answering (Almost) Natural Questions](https://mlsanity.com/paper/2004.13625/) (2020)
- [Document-Level Event Argument Extraction by Conditional Generation](https://mlsanity.com/paper/2104.05919/) (2021)
- [Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction](https://mlsanity.com/paper/2106.09232/) (2021)
- [MAVEN: A Massive General Domain Event Detection Dataset](https://mlsanity.com/paper/2004.13590/) (2020)
- [ASER: A Large-scale Eventuality Knowledge Graph](https://mlsanity.com/paper/1905.00270/) (2019)
- [Meta-Learning with Dynamic-Memory-Based Prototypical Network for Few-Shot Event Detection](https://mlsanity.com/paper/1910.11621/) (2019)
- [Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction](https://mlsanity.com/paper/1909.05360/) (2019)
- [Joint Extraction of Events and Entities within a Document Context](https://mlsanity.com/paper/1609.03632/) (2016)
- [One for All: Neural Joint Modeling of Entities and Events](https://mlsanity.com/paper/1812.00195/) (2018)
- [ESTER: A Machine Reading Comprehension Dataset for Event Semantic Relation Reasoning](https://mlsanity.com/paper/2104.08350/) (2021)
- [Men Are Elected, Women Are Married: Events Gender Bias on Wikipedia](https://mlsanity.com/paper/2106.01601/) (2021)
- [Deep Structured Neural Network for Event Temporal Relation Extraction](https://mlsanity.com/paper/1909.10094/) (2019)
- [Few-Shot Event Detection with Prototypical Amortized Conditional Random Field](https://mlsanity.com/paper/2012.02353/) (2020)
- [Document-level Event Extraction with Efficient End-to-end Learning of Cross-event Dependencies](https://mlsanity.com/paper/2010.12787/) (2020)
- [Adaptive Knowledge-Enhanced Bayesian Meta-Learning for Few-shot Event Detection](https://mlsanity.com/paper/2105.09509/) (2021)

## Built on by (most cited first, 30 of 83)

- [Large Language Models for Generative Information Extraction: A Survey](https://mlsanity.com/paper/2312.17617/) (2023)
- [Evaluating ChatGPT's Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulness](https://mlsanity.com/paper/2304.11633/) (2023)
- [Code4Struct: Code Generation for Few-Shot Event Structure Prediction](https://mlsanity.com/paper/2210.12810/) (2022)
- [Retrieval-Augmented Code Generation for Universal Information Extraction](https://mlsanity.com/paper/2311.02962/) (2023)
- [TextEE: Benchmark, Reevaluation, Reflections, and Future Challenges in Event Extraction](https://mlsanity.com/paper/2311.09562/) (2023)
- [GENEVA: Benchmarking Generalizability for Event Argument Extraction with Hundreds of Event Types and Argument Roles](https://mlsanity.com/paper/2205.12505/) (2022)
- [DICE: Data-Efficient Clinical Event Extraction with Generative Models](https://mlsanity.com/paper/2208.07989/) (2022)
- [AMPERE: AMR-Aware Prefix for Generation-Based Event Argument Extraction Model](https://mlsanity.com/paper/2305.16734/) (2023)
- [The Devil is in the Details: On the Pitfalls of Event Extraction Evaluation](https://mlsanity.com/paper/2306.06918/) (2023)
- [Event Extraction as Question Generation and Answering](https://mlsanity.com/paper/2307.05567/) (2023)
- [TAGPRIME: A Unified Framework for Relational Structure Extraction](https://mlsanity.com/paper/2205.12585/) (2022)
- [Enhanced Chart Understanding in Vision and Language Task via Cross-modal Pre-training on Plot Table Pairs](https://mlsanity.com/paper/2305.18641/) (2023)
- [Pre-trained Language Models for Keyphrase Generation: A Thorough Empirical Study](https://mlsanity.com/paper/2212.10233/) (2022)
- [ULTRA: Unleash LLMs' Potential for Event Argument Extraction through Hierarchical Modeling and Pair-wise Self-Refinement](https://mlsanity.com/paper/2401.13218/) (2024)
- [LLMs in Biomedicine: A study on clinical Named Entity Recognition](https://mlsanity.com/paper/2404.07376/) (2024)
- [Training Multimedia Event Extraction With Generated Images and Captions](https://mlsanity.com/paper/2306.08966/) (2023)
- [Information Extraction in Low-Resource Scenarios: Survey and Perspective](https://mlsanity.com/paper/2202.08063/) (2022)
- [Revisiting Event Argument Extraction: Can EAE Models Learn Better When Being Aware of Event Co-occurrences?](https://mlsanity.com/paper/2306.00502/) (2023)
- [Contextual Label Projection for Cross-Lingual Structured Prediction](https://mlsanity.com/paper/2309.08943/) (2023)
- [Explicit, Implicit, and Scattered: Revisiting Event Extraction to Capture Complex Arguments](https://mlsanity.com/paper/2410.03594/) (2024)
- [Parameter-Efficient Low-Resource Dialogue State Tracking by Prompt Tuning](https://mlsanity.com/paper/2301.10915/) (2023)
- [GenKIE: Robust Generative Multimodal Document Key Information Extraction](https://mlsanity.com/paper/2310.16131/) (2023)
- [Zero-shot Triplet Extraction by Template Infilling](https://mlsanity.com/paper/2212.10708/) (2022)
- [MOKA: Moral Knowledge Augmentation for Moral Event Extraction](https://mlsanity.com/paper/2311.09733/) (2023)
- [Event Detection from Social Media for Epidemic Prediction](https://mlsanity.com/paper/2404.01679/) (2024)
- [SPEED++: A Multilingual Event Extraction Framework for Epidemic Prediction and Preparedness](https://mlsanity.com/paper/2410.18393/) (2024)
- [Controllable Text Generation for Open-Domain Creativity and Fairness](https://mlsanity.com/paper/2209.12099/) (2022)
- [Schema-aware Reference as Prompt Improves Data-Efficient Knowledge Graph Construction](https://mlsanity.com/paper/2210.10709/) (2022)
- [STAR: Boosting Low-Resource Information Extraction by Structure-to-Text Data Generation with Large Language Models](https://mlsanity.com/paper/2305.15090/) (2023)
- [MAVEN-Arg: Completing the Puzzle of All-in-One Event Understanding Dataset with Event Argument Annotation](https://mlsanity.com/paper/2311.09105/) (2023)

## Abstract (arXiv)

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.