  To minimize the accelerating amount of time invested in the biomedical
literature search, numerous approaches for automated knowledge extraction have
been proposed. Relation extraction is one such task where semantic relations
between the entities are identified from the free text. In the biomedical
domain, extraction of regulatory pathways, metabolic processes, adverse drug
reaction or disease models necessitates knowledge from the individual
relations, for example, physical or regulatory interactions between genes,
proteins, drugs, chemical, disease or phenotype. In this paper, we study the
relation extraction task from three major biomedical and clinical tasks, namely
drug-drug interaction, protein-protein interaction, and medical concept
relation extraction. Towards this, we model the relation extraction problem in
multi-task learning (MTL) framework and introduce for the first time the
concept of structured self-attentive network complemented with the adversarial
learning approach for the prediction of relationships from the biomedical and
clinical text. The fundamental notion of MTL is to simultaneously learn
multiple problems together by utilizing the concepts of the shared
representation. Additionally, we also generate the highly efficient single task
model which exploits the shortest dependency path embedding learned over the
attentive gated recurrent unit to compare our proposed MTL models. The
framework we propose significantly improves overall the baselines (deep
learning techniques) and single-task models for predicting the relationships,
without compromising on the performance of all the tasks.

Computation and Language