Zero-Shot Relation Extraction via Reading Comprehension

Omer Levy, Minjoon Seo, Eunsol Choi, Luke Zettlemoyer


Abstract
We show that relation extraction can be reduced to answering simple reading comprehension questions, by associating one or more natural-language questions with each relation slot. This reduction has several advantages: we can (1) learn relation-extraction models by extending recent neural reading-comprehension techniques, (2) build very large training sets for those models by combining relation-specific crowd-sourced questions with distant supervision, and even (3) do zero-shot learning by extracting new relation types that are only specified at test-time, for which we have no labeled training examples. Experiments on a Wikipedia slot-filling task demonstrate that the approach can generalize to new questions for known relation types with high accuracy, and that zero-shot generalization to unseen relation types is possible, at lower accuracy levels, setting the bar for future work on this task.
Anthology ID:
K17-1034
Volume:
Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venue:
CoNLL
SIG:
SIGNLL
Publisher:
Association for Computational Linguistics
Note:
Pages:
333–342
Language:
URL:
https://www.aclweb.org/anthology/K17-1034
DOI:
10.18653/v1/K17-1034
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PDF:
http://aclanthology.lst.uni-saarland.de/K17-1034.pdf