ZeroShotCeres: Zero-Shot Relation Extraction from Semi-Structured Webpages

Colin Lockard, Prashant Shiralkar, Xin Luna Dong, Hannaneh Hajishirzi


Abstract
In many documents, such as semi-structured webpages, textual semantics are augmented with additional information conveyed using visual elements including layout, font size, and color. Prior work on information extraction from semi-structured websites has required learning an extraction model specific to a given template via either manually labeled or distantly supervised data from that template. In this work, we propose a solution for “zero-shot” open-domain relation extraction from webpages with a previously unseen template, including from websites with little overlap with existing sources of knowledge for distant supervision and websites in entirely new subject verticals. Our model uses a graph neural network-based approach to build a rich representation of text fields on a webpage and the relationships between them, enabling generalization to new templates. Experiments show this approach provides a 31% F1 gain over a baseline for zero-shot extraction in a new subject vertical.
Anthology ID:
2020.acl-main.721
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Editors:
Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
8105–8117
Language:
URL:
https://aclanthology.org/2020.acl-main.721
DOI:
10.18653/v1/2020.acl-main.721
Bibkey:
Cite (ACL):
Colin Lockard, Prashant Shiralkar, Xin Luna Dong, and Hannaneh Hajishirzi. 2020. ZeroShotCeres: Zero-Shot Relation Extraction from Semi-Structured Webpages. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 8105–8117, Online. Association for Computational Linguistics.
Cite (Informal):
ZeroShotCeres: Zero-Shot Relation Extraction from Semi-Structured Webpages (Lockard et al., ACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.721.pdf
Video:
 http://slideslive.com/38929243