  Document-level relation extraction (DocRE) aims to identify semantic labels
among entities within a single document. One major challenge of DocRE is to dig
decisive details regarding a specific entity pair from long text. However, in
many cases, only a fraction of text carries required information, even in the
manually labeled supporting evidence. To better capture and exploit instructive
information, we propose a novel expLicit syntAx Refinement and Subsentence
mOdeliNg based framework (LARSON). By introducing extra syntactic information,
LARSON can model subsentences of arbitrary granularity and efficiently screen
instructive ones. Moreover, we incorporate refined syntax into text
representations which further improves the performance of LARSON. Experimental
results on three benchmark datasets (DocRED, CDR, and GDA) demonstrate that
LARSON significantly outperforms existing methods.
