A Human-machine Interface for Few-shot Rule Synthesis for Information Extraction
Robert Vacareanu, George C.G. Barbosa, Enrique Noriega-Atala, Gus Hahn-Powell, Rebecca Sharp, Marco A. Valenzuela-Escárcega, Mihai Surdeanu
Abstract
We propose a system that assists a user in constructing transparent information extraction models, consisting of patterns (or rules) written in a declarative language, through program synthesis.Users of our system can specify their requirements through the use of examples,which are collected with a search interface.The rule-synthesis system proposes rule candidates and the results of applying them on a textual corpus; the user has the option to accept the candidate, request another option, or adjust the examples provided to the system.Through an interactive evaluation, we show that our approach generates high-precision rules even in a 1-shot setting. On a second evaluation on a widely-used relation extraction dataset (TACRED), our method generates rules that outperform considerably manually written patterns.Our code, demo, and documentation is available at https://clulab.github.io/odinsynth.- Anthology ID:
- 2022.naacl-demo.8
- Volume:
- Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: System Demonstrations
- Month:
- July
- Year:
- 2022
- Address:
- Hybrid: Seattle, Washington + Online
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 64–70
- Language:
- URL:
- https://aclanthology.org/2022.naacl-demo.8
- DOI:
- 10.18653/v1/2022.naacl-demo.8
- Cite (ACL):
- Robert Vacareanu, George C.G. Barbosa, Enrique Noriega-Atala, Gus Hahn-Powell, Rebecca Sharp, Marco A. Valenzuela-Escárcega, and Mihai Surdeanu. 2022. A Human-machine Interface for Few-shot Rule Synthesis for Information Extraction. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: System Demonstrations, pages 64–70, Hybrid: Seattle, Washington + Online. Association for Computational Linguistics.
- Cite (Informal):
- A Human-machine Interface for Few-shot Rule Synthesis for Information Extraction (Vacareanu et al., NAACL 2022)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2022.naacl-demo.8.pdf
- Data
- TACRED