Abstract
Document-level Relation Extraction (DocRE), which aims to extract relations from a long context, is a critical challenge in achieving fine-grained structural comprehension and generating interpretable document representations. Inspired by recent advances in in-context learning capabilities emergent from large language models (LLMs), such as ChatGPT, we aim to design an automated annotation method for DocRE with minimum human effort. Unfortunately, vanilla in-context learning is infeasible for DocRE due to the plenty of predefined fine-grained relation types and the uncontrolled generations of LLMs. To tackle this issue, we propose a method integrating an LLM and a natural language inference (NLI) module to generate relation triples, thereby augmenting document-level relation datasets. We demonstrate the effectiveness of our approach by introducing an enhanced dataset known as DocGNRE, which excels in re-annotating numerous long-tail relation types. We are confident that our method holds the potential for broader applications in domain-specific relation type definitions and offers tangible benefits in advancing generalized language semantic comprehension.- Anthology ID:
- 2023.emnlp-main.334
- Volume:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5495–5505
- Language:
- URL:
- https://aclanthology.org/2023.emnlp-main.334
- DOI:
- 10.18653/v1/2023.emnlp-main.334
- Cite (ACL):
- Junpeng Li, Zixia Jia, and Zilong Zheng. 2023. Semi-automatic Data Enhancement for Document-Level Relation Extraction with Distant Supervision from Large Language Models. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 5495–5505, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Semi-automatic Data Enhancement for Document-Level Relation Extraction with Distant Supervision from Large Language Models (Li et al., EMNLP 2023)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/2023.emnlp-main.334.pdf