# Zero-Shot Relation Extraction via Reading Comprehension

arXiv: https://arxiv.org/abs/1706.04115 · HTML: https://mlsanity.com/paper/1706.04115/

## Key facts

- Cited by 426 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2017.

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## Abstract (arXiv)

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.