# Sentence-level Event Detection without Triggers via Prompt Learning and Machine Reading Comprehension

arXiv: https://arxiv.org/abs/2306.14176 · HTML: https://mlsanity.com/paper/2306.14176/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 31 references that are in the map.
- First posted on arXiv 25 Jun 2023.

## Builds on (most cited first, 5 of 5)

- [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)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Bidirectional Attention Flow for Machine Comprehension](https://mlsanity.com/paper/1611.01603/) (2016)
- [Trigger-free Event Detection via Derangement Reading Comprehension](https://mlsanity.com/paper/2208.09659/) (2022)

## Abstract (arXiv)

The traditional way of sentence-level event detection involves two important subtasks: trigger identification and trigger classifications, where the identified event trigger words are used to classify event types from sentences. However, trigger classification highly depends on abundant annotated trigger words and the accuracy of trigger identification. In a real scenario, annotating trigger words is time-consuming and laborious. For this reason, we propose a trigger-free event detection model, which transforms event detection into a two-tower model based on machine reading comprehension and prompt learning. Compared to existing trigger-based and trigger-free methods, experimental studies on two event detection benchmark datasets (ACE2005 and MAVEN) have shown that the proposed approach can achieve competitive performance.