@inproceedings{vacareanu-etal-2022-human,
title = "A Human-machine Interface for Few-shot Rule Synthesis for Information Extraction",
author = "Vacareanu, Robert and
Barbosa, George C.G. and
Noriega-Atala, Enrique and
Hahn-Powell, Gus and
Sharp, Rebecca and
Valenzuela-Esc{\'a}rcega, Marco A. and
Surdeanu, Mihai",
editor = "Hajishirzi, Hannaneh and
Ning, Qiang and
Sil, Avi",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: System Demonstrations",
month = jul,
year = "2022",
address = "Hybrid: Seattle, Washington + Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-demo.8",
doi = "10.18653/v1/2022.naacl-demo.8",
pages = "64--70",
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 \url{https://clulab.github.io/odinsynth}.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T A Human-machine Interface for Few-shot Rule Synthesis for Information Extraction
%A Vacareanu, Robert
%A Barbosa, George C.G.
%A Noriega-Atala, Enrique
%A Hahn-Powell, Gus
%A Sharp, Rebecca
%A Valenzuela-Escárcega, Marco A.
%A Surdeanu, Mihai
%Y Hajishirzi, Hannaneh
%Y Ning, Qiang
%Y Sil, Avi
%S Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: System Demonstrations
%D 2022
%8 July
%I Association for Computational Linguistics
%C Hybrid: Seattle, Washington + Online
%F vacareanu-etal-2022-human
%X 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.
%R 10.18653/v1/2022.naacl-demo.8
%U https://aclanthology.org/2022.naacl-demo.8
%U https://doi.org/10.18653/v1/2022.naacl-demo.8
%P 64-70
Markdown (Informal)
[A Human-machine Interface for Few-shot Rule Synthesis for Information Extraction](https://aclanthology.org/2022.naacl-demo.8) (Vacareanu et al., NAACL 2022)
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.