Papers with event detection task
KiPT: Knowledge-injected Prompt Tuning for Event Detection (2022.coling-1)
Copied to clipboard
| Challenge: | Existing prompt-based methods may suffer from low precision because they lack event-related semantic knowledge. |
| Approach: | They propose a Knowledge-injected Prompt Tuning model to improve prompt tuning . event detection aims to detect events from text by identifying and classifying event triggers . |
| Outcome: | The proposed model outperforms baseline models in few-shot scenarios. |
Unsupervised Domain Adaptation for Event Detection using Domain-specific Adapters (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to ED are limited due to complexity of textual data and domain shift problem. |
| Approach: | They propose to use domain adapter-based Adaptation framework to improve event detection across domains. |
| Outcome: | The proposed framework significantly boosts the performance on target domains. |
A Search-based Neural Model for Biomedical Nested and Overlapping Event Detection (D19-1)
Copied to clipboard
| Challenge: | Existing structured prediction tasks target nested and overlapping events . a new structured prediction model is proposed that uses a relation graph to detect overlapping and nesting events. |
| Approach: | They propose a search-based neural network structured prediction model that treats the task as a searching problem on a relation graph of trigger-argument structures. |
| Outcome: | The proposed model performs comparable to the state-of-the-art model Turku Event Extraction System (TEES) on the BioNLP Cancer Genetics (CG) Shared Task 2013 without the use of syntactic and hand-engineered features. |
Language Models Lack Temporal Generalization and Bigger is Not Better (2025.findings-acl)
Copied to clipboard
| Challenge: | 450 encoder models are fine-tuned on 15 data splits on a task to detect events in Early Modern Dutch archival texts. |
| Approach: | They propose to fine tune six encoder models that have been pretrained with very different data on a task in Early Modern Dutch archival texts. |
| Outcome: | The proposed model is fine tuned with 5 seeds on 15 different data splits and reaches highest F1 performance. |