  Most work in relation extraction forms a prediction by looking at a short
span of text within a single sentence containing a single entity pair mention.
This approach often does not consider interactions across mentions, requires
redundant computation for each mention pair, and ignores relationships
expressed across sentence boundaries. These problems are exacerbated by the
document- (rather than sentence-) level annotation common in biological text.
In response, we propose a model which simultaneously predicts relationships
between all mention pairs in a document. We form pairwise predictions over
entire paper abstracts using an efficient self-attention encoder. All-pairs
mention scores allow us to perform multi-instance learning by aggregating over
mentions to form entity pair representations. We further adapt to settings
without mention-level annotation by jointly training to predict named entities
and adding a corpus of weakly labeled data. In experiments on two Biocreative
benchmark datasets, we achieve state of the art performance on the Biocreative
V Chemical Disease Relation dataset for models without external KB resources.
We also introduce a new dataset an order of magnitude larger than existing
human-annotated biological information extraction datasets and more accurate
than distantly supervised alternatives.
