Name
Abstract | ||
|---|---|---|
Event argument extraction (EAE) is an important task for information extraction to discover specific argument roles. In this study, we cast EAE as a question-based cloze task and empirically analyze fixed discrete token template performance. As generating human-annotated question templates is often time-consuming and labor-intensive, we further propose a novel approach called “Learning to Ask,” which can learn optimized question templates for EAE without human annotations. Experiments using the ACE-2005 dataset demonstrate that our method based on optimized questions achieves state-of-the-art performance in both the few-shot and supervised settings. |
Year | Venue | Keywords |
|---|---|---|
2022 | AAAI Conference on Artificial Intelligence | Prompt,Event Argument Extraction,Event Extraction |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongbin Ye | 1 | 8 | 2.89 |
| Ningyu Zhang | 2 | 0 | 0.34 |
| Zhen Bi | 3 | 0 | 3.38 |
| Shumin Deng | 4 | 32 | 10.61 |
| Chuanqi Tan | 5 | 29 | 9.25 |
| Hui Chen | 6 | 7 | 3.13 |
| Fei Huang | 7 | 2 | 7.54 |
| Huanhuan Chen | 8 | 731 | 101.79 |