  This paper presents our participation in the AGAC Track from the 2019 BioNLP
Open Shared Tasks. We provide a solution for Task 3, which aims to extract
"gene - function change - disease" triples, where "gene" and "disease" are
mentions of particular genes and diseases respectively and "function change" is
one of four pre-defined relationship types. Our system extends BERT (Devlin et
al., 2018), a state-of-the-art language model, which learns contextual language
representations from a large unlabelled corpus and whose parameters can be
fine-tuned to solve specific tasks with minimal additional architecture. We
encode the pair of mentions and their textual context as two consecutive
sequences in BERT, separated by a special symbol. We then use a single linear
layer to classify their relationship into five classes (four pre-defined, as
well as 'no relation'). Despite considerable class imbalance, our system
significantly outperforms a random baseline while relying on an extremely
simple setup with no specially engineered features.
