Papers with ACE-2005 dataset
Document Embedding Enhanced Event Detection with Hierarchical and Supervised Attention (P18-2)
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| Challenge: | Existing methods for event detection use sentence-level contextual information. |
| Approach: | They propose a document embedding enhanced bi-RNN model to detect events in sentences . they use hierarchical and supervised attention based RNN to learn document embeds . |
| Outcome: | The proposed model compares with state-of-the-art models on a ACE-2005 dataset. |
Zero-shot Label-Aware Event Trigger and Argument Classification (2021.findings-acl)
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| Challenge: | Existing work on event extraction relies on labor-intensive annotation, ignoring semantic meaning of event types' labels. |
| Approach: | They propose a zero-shot event extraction approach that first identifies events with existing tools and then maps them to a given taxonomy of event types in a no-shot manner. |
| Outcome: | The proposed approach doubles the performance of previous approaches on a ACE-2005 dataset . it leverages label representations induced by pre-trained language models and maps events to the target types . |
Exploiting Contextual Information via Dynamic Memory Network for Event Detection (D18-1)
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| Challenge: | Existing methods for event detection only process context once . a multi-hop mechanism to capture contextual information improves performance . |
| Approach: | They propose to use dynamic memory network to capture contextual information . they propose to model event triggers by identifying word or phrase which most represents it . |
| Outcome: | The proposed model achieves best F1 score compared to the state-of-the-art models. |