Entangled Relations: Leveraging NLI and Meta-analysis to Enhance Biomedical Relation Extraction
Abstract: Recent research efforts have explored the potential of leveraging natural language inference (NLI) techniques to enhance relation extraction (RE). In this vein, we introduce MetaEntail-RE, a novel adaptation method that harnesses NLI principles to enhance RE performance.
Our approach follows past works by verbalizing relation classes into class-indicative hypotheses, aligning a traditionally multi-class classification task to one of textual entailment. We introduce three key enhancements: (1) Meta-class analysis which, instead of labeling non-entailed premise-hypothesis pairs with the less informative "neutral" entailment label, provides additional context by analyzing overarching meta-relationships between classes; (2) Feasible hypothesis filtering, which removes unlikely hypotheses from consideration based on pairs of entity types; and (3) Group-based prediction selection, which further improves performance by selecting highly confident predictions. MetaEntail-RE is conceptually simple and empirically powerful, yielding significant improvements over conventional relation extraction techniques and other NLI formulations. We observe F1 gains of 17.6 points on BioRED and 13.4 points on ReTACRED when compared to conventional methods, underscoring the versatility of MetaEntail-RE across both biomedical and general domains.
Paper Type: Long
Research Area: Information Extraction
Research Area Keywords: Relation extraction, information extraction, entailment
Contribution Types: NLP engineering experiment, Approaches to low-resource settings
Languages Studied: English
Submission Number: 977
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