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Distant supervision uses triple facts in knowledge graphs to label a corpusfor relation extraction, leading to wrong labeling and long-tail problems. Someworks use the hierarchy of relations for knowledge transfer to long-tailrelations. However, a coarse-grained relation often implies only an attribute(e.g., domain or topic) of the distant fact, making it hard to discriminaterelations based solely on sentence semantics. One solution is resorting toentity types, but open questions remain about how to fully leverage theinformation of entity types and how to align multi-granular entity types withsentences. In this work, we propose a novel model to enrichdistantly-supervised sentences with entity types. It consists of (1) a pairwisetype-enriched sentence encoding module injecting both context-free and -relatedbackgrounds to alleviate sentence-level wrong labeling, and (2) a hierarchicaltype-sentence alignment module enriching a sentence with the triple fact'sbasic attributes to support long-tail relations. Our model achieves newstate-of-the-art results in overall and long-tail performance on benchmarks.

Hierarchical Relation-Guided Type-Sentence Alignment for Long-Tail Relation Extraction with Distant Supervision