Learning to Generate Rules for Realistic Few-Shot Relation Classification: An Encoder-Decoder Approach
Published in Findings of the Association for Computational Linguistics: EMNLP, 2024
We propose a neuro-symbolic approach for realistic few-shot relation classification via rules. Instead of building neural models to predict relations, we design them to output straightforward rules that can be used to extract relations. The rules are generated using custom T5-style Encoder-Decoder Language Models. Crucially, our rules are fully interpretable and pliable (i.e., humans can easily modify them to boost performance). Through a combination of rules generated by these models along with a very effective, novel baseline, we demonstrate a few-shot relation-classification performance that is comparable to or stronger than the state of the art on the Few-Shot TACRED and NYT29 benchmarks while increasing interpretability and maintaining pliability.
Recommended citation: Mayank Singh and Eduardo Blanco. 2024. Learning to Generate Rules for Realistic Few-Shot Relation Classification: An Encoder-Decoder Approach. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 5907–5921, Miami, Florida, USA.
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