  Document-level relation extraction (DocRE) aims to determine the relation
between two entities from a document of multiple sentences. Recent studies
typically represent the entire document by sequence- or graph-based models to
predict the relations of all entity pairs. However, we find that such a model
is not robust and exhibits bizarre behaviors: it predicts correctly when an
entire test document is fed as input, but errs when non-evidence sentences are
removed. To this end, we propose a Sentence Importance Estimation and Focusing
(SIEF) framework for DocRE, where we design a sentence importance score and a
sentence focusing loss, encouraging DocRE models to focus on evidence
sentences. Experimental results on two domains show that our SIEF not only
improves overall performance, but also makes DocRE models more robust.
Moreover, SIEF is a general framework, shown to be effective when combined with
a variety of base DocRE models.
