Event Argument Extraction with Enriched Prompts
Fuente:
arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916563018317824 |
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| author | Liang, Chen |
| author_facet | Liang, Chen |
| contents | This work aims to delve deeper into prompt-based event argument extraction (EAE) models. We explore the impact of incorporating various types of information into the prompt on model performance, including trigger, other role arguments for the same event, and role arguments across multiple events within the same document. Further, we provide the best possible performance that the prompt-based EAE model can attain and demonstrate such models can be further optimized from the perspective of the training objective. Experiments are carried out on three small language models and two large language models in RAMS. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_06825 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Event Argument Extraction with Enriched Prompts Liang, Chen Computation and Language This work aims to delve deeper into prompt-based event argument extraction (EAE) models. We explore the impact of incorporating various types of information into the prompt on model performance, including trigger, other role arguments for the same event, and role arguments across multiple events within the same document. Further, we provide the best possible performance that the prompt-based EAE model can attain and demonstrate such models can be further optimized from the perspective of the training objective. Experiments are carried out on three small language models and two large language models in RAMS. |
| title | Event Argument Extraction with Enriched Prompts |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2501.06825 |