  Distant supervision uses triple facts in knowledge graphs to label a corpus
for relation extraction, leading to wrong labeling and long-tail problems. Some
works use the hierarchy of relations for knowledge transfer to long-tail
relations. However, a coarse-grained relation often implies only an attribute
(e.g., domain or topic) of the distant fact, making it hard to discriminate
relations based solely on sentence semantics. One solution is resorting to
entity types, but open questions remain about how to fully leverage the
information of entity types and how to align multi-granular entity types with
sentences. In this work, we propose a novel model to enrich
distantly-supervised sentences with entity types. It consists of (1) a pairwise
type-enriched sentence encoding module injecting both context-free and -related
backgrounds to alleviate sentence-level wrong labeling, and (2) a hierarchical
type-sentence alignment module enriching a sentence with the triple fact's
basic attributes to support long-tail relations. Our model achieves new
state-of-the-art results in overall and long-tail performance on benchmarks.
