Neural Extractive Search

Shauli Ravfogel, Hillel Taub-Tabib, Yoav Goldberg


Abstract
Domain experts often need to extract structured information from large corpora. We advocate for a search paradigm called “extractive search”, in which a search query is enriched with capture-slots, to allow for such rapid extraction. Such an extractive search system can be built around syntactic structures, resulting in high-precision, low-recall results. We show how the recall can be improved using neural retrieval and alignment. The goals of this paper are to concisely introduce the extractive-search paradigm; and to demonstrate a prototype neural retrieval system for extractive search and its benefits and potential. Our prototype is available at https://spike.neural-sim.apps.allenai.org/ and a video demonstration is available at https://vimeo.com/559586687.
Anthology ID:
2021.acl-demo.25
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
210–217
Language:
URL:
https://aclanthology.org/2021.acl-demo.25
DOI:
10.18653/v1/2021.acl-demo.25
Bibkey:
Cite (ACL):
Shauli Ravfogel, Hillel Taub-Tabib, and Yoav Goldberg. 2021. Neural Extractive Search. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations, pages 210–217, Online. Association for Computational Linguistics.
Cite (Informal):
Neural Extractive Search (Ravfogel et al., ACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-demo.25.pdf
Video:
 https://aclanthology.org/2021.acl-demo.25.mp4