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Extracting mentions of Adverse Drug Events (ADEs) and the potential relationships among them from clinical textual data remains challenging tasks due to the following issues: (1) many ADEs mentions have multiple relations, also known as the multi-head issue, and (2) many ADEs relations contain discontinuous mentions. To deal with these problems, in this paper, we propose a Multi-Task Transfer Learning-based method for ADEs extraction, called MTTLADE. Firstly, the MTTLADE system converts the ADEs extraction task to a dual-task sequence labelling which includes ADEs source mention extraction (ADE-SE) and ADEs attribute-relation extraction (ADE-Att-RE) tasks. The ADE-SE task aims at extracting the source mentions that are likely related to at least one relation, while the ADE-Att-RE task consists in linking the previously identified source mentions to their target attributes and relation types by adopting a …