# Are Triggers Needed for Document-Level Event Extraction?

arXiv: https://arxiv.org/abs/2411.08708 · HTML: https://mlsanity.com/paper/2411.08708/

## Key facts

- Cited by 1 papers in the map (data through 7 Oct 2026).
- Builds on 25 of its 49 references that are in the map.
- First posted on arXiv 13 Nov 2024.

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?](https://mlsanity.com/paper/2202.12837/) (2022)
- [ERASER: A Benchmark to Evaluate Rationalized NLP Models](https://mlsanity.com/paper/1911.03429/) (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)
- [Document-Level Event Argument Extraction by Conditional Generation](https://mlsanity.com/paper/2104.05919/) (2021)
- [SciREX: A Challenge Dataset for Document-Level Information Extraction](https://mlsanity.com/paper/2005.00512/) (2020)
- [MAVEN: A Massive General Domain Event Detection Dataset](https://mlsanity.com/paper/2004.13590/) (2020)
- [Revisiting Relation Extraction in the era of Large Language Models](https://mlsanity.com/paper/2305.05003/) (2023)
- [DEGREE: A Data-Efficient Generation-Based Event Extraction Model](https://mlsanity.com/paper/2108.12724/) (2021)
- [Multi-Sentence Argument Linking](https://mlsanity.com/paper/1911.03766/) (2019)
- [Code4Struct: Code Generation for Few-Shot Event Structure Prediction](https://mlsanity.com/paper/2210.12810/) (2022)
- [Prompt for Extraction? PAIE: Prompting Argument Interaction for Event Argument Extraction](https://mlsanity.com/paper/2202.12109/) (2022)
- [Can Rationalization Improve Robustness?](https://mlsanity.com/paper/2204.11790/) (2022)
- [Document-level Event Extraction via Heterogeneous Graph-based Interaction Model with a Tracker](https://mlsanity.com/paper/2105.14924/) (2021)
- [Iterative Document-level Information Extraction via Imitation Learning](https://mlsanity.com/paper/2210.06600/) (2022)
- [Automatic Error Analysis for Document-level Information Extraction](https://mlsanity.com/paper/2209.07442/) (2022)
- [CMNEE: A Large-Scale Document-Level Event Extraction Dataset based on Open-Source Chinese Military News](https://mlsanity.com/paper/2404.12242/) (2024)
- [Document-Level Multi-Event Extraction with Event Proxy Nodes and Hausdorff Distance Minimization](https://mlsanity.com/paper/2305.18926/) (2023)
- [TREND: Trigger-Enhanced Relation-Extraction Network for Dialogues](https://mlsanity.com/paper/2108.13811/) (2021)
- [On Event Individuation for Document-Level Information Extraction](https://mlsanity.com/paper/2212.09702/) (2022)
- [Probing Representations for Document-level Event Extraction](https://mlsanity.com/paper/2310.15316/) (2023)

## Built on by (most cited first, 1 of 1)

- [ChunkWise LoRA: Adaptive Sequence Partitioning for Memory-Efficient Low-Rank Adaptation and Accelerated LLM Inference](https://mlsanity.com/paper/2601.21109/) (2026)

## Abstract (arXiv)

Most existing work on event extraction has focused on sentence-level texts and presumes the identification of a trigger-span -- a word or phrase in the input that evokes the occurrence of an event of interest. Event arguments are then extracted with respect to the trigger. Indeed, triggers are treated as integral to, and trigger detection as an essential component of, event extraction. In this paper, we provide the first investigation of the role of triggers for the more difficult and much less studied task of document-level event extraction. We analyze their usefulness in multiple end-to-end and pipelined transformer-based event extraction models for three document-level event extraction datasets, measuring performance using triggers of varying quality (human-annotated, LLM-generated, keyword-based, and random). We find that whether or not systems benefit from explicitly extracting triggers depends both on dataset characteristics (i.e. the typical number of events per document) and task-specific information available during extraction (i.e. natural language event schemas). Perhaps surprisingly, we also observe that the mere existence of triggers in the input, even random ones, is important for prompt-based in-context learning approaches to the task.