  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) Instead of labeling non-entailed
premise-hypothesis pairs with the uninformative "neutral" entailment label, we
introduce meta-class analysis, which provides additional context by analyzing
overarching meta relationships between classes when assigning entailment
labels; (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. Our experimental results
underscore the versatility of MetaEntail-RE, demonstrating performance gains
across both biomedical and general domains.
