Label Verbalization and Entailment for Effective Zero- and Few-Shot Relation Extraction

• URL: http://arxiv.org/abs/2109.03659v1
• Date: Wed, 8 Sep 2021 14:04:50 GMT
• Title: Label Verbalization and Entailment for Effective Zero- and Few-Shot Relation Extraction
• Authors: Oscar Sainz, Oier Lopez de Lacalle, Gorka Labaka, Ander Barrena and Eneko Agirre
• Abstract summary: In this work we reformulate relation extraction as an entailment task, with simple, hand-made, verbalizations of relations produced in less than 15 min per relation. The system relies on a pretrained textual entailment engine which is run as-is (no training examples, zero-shot) or further fine-tuned on labeled examples (few-shot or fully trained) In experiments on TACRED we attain 63% F1 zero-shot, 69% with 16 examples per relation (17% points better than the best supervised system on the same conditions), and only 4 points short to the state-of-the
• Score: 25.151235270747804
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Relation extraction systems require large amounts of labeled examples which are costly to annotate. In this work we reformulate relation extraction as an entailment task, with simple, hand-made, verbalizations of relations produced in less than 15 min per relation. The system relies on a pretrained textual entailment engine which is run as-is (no training examples, zero-shot) or further fine-tuned on labeled examples (few-shot or fully trained). In our experiments on TACRED we attain 63% F1 zero-shot, 69% with 16 examples per relation (17% points better than the best supervised system on the same conditions), and only 4 points short to the state-of-the-art (which uses 20 times more training data). We also show that the performance can be improved significantly with larger entailment models, up to 12 points in zero-shot, allowing to report the best results to date on TACRED when fully trained. The analysis shows that our few-shot systems are specially effective when discriminating between relations, and that the performance difference in low data regimes comes mainly from identifying no-relation cases.

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