# DICE: Data-Efficient Clinical Event Extraction with Generative Models

arXiv: https://arxiv.org/abs/2208.07989 · HTML: https://mlsanity.com/paper/2208.07989/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 66 references that are in the map.
- First posted on arXiv 16 Aug 2022.

## Builds on (most cited first, 26 of 26)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks](https://mlsanity.com/paper/1506.03099/) (2015)
- [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)
- [A Unified Generative Framework for Various NER Subtasks](https://mlsanity.com/paper/2106.01223/) (2021)
- [Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction](https://mlsanity.com/paper/2106.09232/) (2021)
- [COCO-LM: Correcting and Contrasting Text Sequences for Language Model Pretraining](https://mlsanity.com/paper/2102.08473/) (2021)
- [Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Network](https://mlsanity.com/paper/2006.05702/) (2020)
- [DEGREE: A Data-Efficient Generation-Based Event Extraction Model](https://mlsanity.com/paper/2108.12724/) (2021)
- [A Simple but Tough-to-Beat Data Augmentation Approach for Natural Language Understanding and Generation](https://mlsanity.com/paper/2009.13818/) (2020)
- [Prompt for Extraction? PAIE: Prompting Argument Interaction for Event Argument Extraction](https://mlsanity.com/paper/2202.12109/) (2022)
- [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)
- [Multilingual Generative Language Models for Zero-Shot Cross-Lingual Event Argument Extraction](https://mlsanity.com/paper/2203.08308/) (2022)
- [Dynamic Prefix-Tuning for Generative Template-based Event Extraction](https://mlsanity.com/paper/2205.06166/) (2022)
- [Document-level Entity-based Extraction as Template Generation](https://mlsanity.com/paper/2109.04901/) (2021)
- [Query and Extract: Refining Event Extraction as Type-oriented Binary Decoding](https://mlsanity.com/paper/2110.07476/) (2021)
- [Ontology-enhanced Prompt-tuning for Few-shot Learning](https://mlsanity.com/paper/2201.11332/) (2022)
- [A Neural Multi-Task Learning Framework to Jointly Model Medical Named Entity Recognition and Normalization](https://mlsanity.com/paper/1812.06081/) (2018)
- [EventPlus: A Temporal Event Understanding Pipeline](https://mlsanity.com/paper/2101.04922/) (2021)
- [Domain Knowledge Empowered Structured Neural Net for End-to-End Event Temporal Relation Extraction](https://mlsanity.com/paper/2009.07373/) (2020)
- [HyperExpan: Taxonomy Expansion with Hyperbolic Representation Learning](https://mlsanity.com/paper/2109.10500/) (2021)
- [Recurrent Interaction Network for Jointly Extracting Entities and Classifying Relations](https://mlsanity.com/paper/2005.00162/) (2020)

## Built on by (most cited first, 20 of 20)

- [Large Language Models for Generative Information Extraction: A Survey](https://mlsanity.com/paper/2312.17617/) (2023)
- [Can NLI Provide Proper Indirect Supervision for Low-resource Biomedical Relation Extraction?](https://mlsanity.com/paper/2212.10784/) (2022)
- [LLMs in Biomedicine: A study on clinical Named Entity Recognition](https://mlsanity.com/paper/2404.07376/) (2024)
- [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)
- [Mitigating Bias for Question Answering Models by Tracking Bias Influence](https://mlsanity.com/paper/2310.08795/) (2023)
- [CliBench: A Multifaceted and Multigranular Evaluation of Large Language Models for Clinical Decision Making](https://mlsanity.com/paper/2406.09923/) (2024)
- [Memorize and Rank: Elevating Large Language Models for Clinical Diagnosis Prediction](https://mlsanity.com/paper/2501.17326/) (2025)
- [Improving Event Definition Following For Zero-Shot Event Detection](https://mlsanity.com/paper/2403.02586/) (2024)
- [Characterizing Information Seeking Events in Health-Related Social Discourse](https://mlsanity.com/paper/2308.09156/) (2023)
- [Event Extraction in Large Language Model](https://mlsanity.com/paper/2512.19537/) (2025)
- [Extracting Social Determinants of Health from Pediatric Patient Notes Using Large Language Models: Novel Corpus and Methods](https://mlsanity.com/paper/2404.00826/) (2024)
- [MedDec: A Dataset for Extracting Medical Decisions from Discharge Summaries](https://mlsanity.com/paper/2408.12980/) (2024)
- [SEOE: A Scalable and Reliable Semantic Evaluation Framework for Open Domain Event Detection](https://mlsanity.com/paper/2503.03303/) (2025)
- [MIDDAG: Where Does Our News Go? Investigating Information Diffusion via Community-Level Information Pathways](https://mlsanity.com/paper/2310.02529/) (2023)
- [Spurious Correlations and Beyond: Understanding and Mitigating Shortcut Learning in SDOH Extraction with Large Language Models](https://mlsanity.com/paper/2506.00134/) (2025)
- [EXCEEDS: Extracting Complex Events via Nugget-based Grid Modeling in Scientific Domain](https://mlsanity.com/paper/2406.14075/) (2024)
- [Inferring from Logits: Exploring Best Practices for Decoding-Free Generative Candidate Selection](https://mlsanity.com/paper/2501.17338/) (2025)
- [Improving Information Extraction with Learned Queries](https://mlsanity.com/paper/2608.31058/) (2026)
- [Synthesized Annotation Guidelines are Knowledge-Lite Boosters for Clinical Information Extraction](https://mlsanity.com/paper/2504.02871/) (2025)

## Abstract (arXiv)

Event extraction for the clinical domain is an under-explored research area. The lack of training data along with the high volume of domain-specific terminologies with vague entity boundaries makes the task especially challenging. In this paper, we introduce DICE, a robust and data-efficient generative model for clinical event extraction. DICE frames event extraction as a conditional generation problem and introduces a contrastive learning objective to accurately decide the boundaries of biomedical mentions. DICE also trains an auxiliary mention identification task jointly with event extraction tasks to better identify entity mention boundaries, and further introduces special markers to incorporate identified entity mentions as trigger and argument candidates for their respective tasks. To benchmark clinical event extraction, we compose MACCROBAT-EE, the first clinical event extraction dataset with argument annotation, based on an existing clinical information extraction dataset MACCROBAT. Our experiments demonstrate state-of-the-art performances of DICE for clinical and news domain event extraction, especially under low data settings.