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 unified sequence labelling. Then, it uses the multi-task transfer learning (MTTL) based approach to process the two proposed tasks simultaneously. The MTTL adopts a shared representation obtained from the pre-trained language model learned through transformer architecture and ends up with task-specific fine-tuning. This allows the MTTLADE system to yield more generalized representation across the tasks. Finally, MTTLADE produces sequences for each task from the generated model so as to extract ADEs mentions and relations. Experimental evaluations conducted on two datasets provided by the TAC 2017 and n2c2 2018 shared tasks show the effectiveness and generalizability of MTTLADE. The proposed MTTLADE system significantly outperforms the state-of-the-art ones on both datasets. The results also show that combining transfer and multi-task learning makes MTTLADE more effective for solving the multi-head issue and extracting intricate ADEs.

由于以下问题，从临床文本数据中提取不良药品事件（ADEs）的提及及其之间的潜在关系仍然是一项艰巨的任务：（1）许多ADE提及的药品具有多重关系，也称为多头问题；和（2 ）许多ADE关系中包含不连续的提及。为了解决这些问题，在本文中，我们提出了一个中号ulti-牛逼问牛逼转让（BOT）大号为基于收益法ADEs的提取，称为MTTLADE。首先，MTTLADE系统将ADEs提取任务转换为双任务序列标记，其中包括ADEs源代码提取（ADE-SE）和ADEs属性关系提取（ADE-Att-RE）任务。ADE-SE任务旨在提取可能与至少一个关系相关的源提及，而ADE-Att-RE任务在于通过采用统一的序列标记将先前标识的源提及与其目标属性和关系类型链接起来。然后，它使用基于多任务传输学习（MTTL）的方法来同时处理两个建议的任务。MTTL采用从通过变压器体系结构学习的预训练语言模型获得的共享表示形式，并最终完成了特定于任务的微调。这允许MTTLADE系统在整个任务中产生更通用的表示。最后，MTTLADE从生成的模型为每个任务生成序列，以便提取ADE的提及和关系。在TAC 2017和n2c2 2018共享任务提供的两个数据集上进行的实验评估显示了MTTLADE的有效性和通用性。拟议的MTTLADE系统在两个数据集上均明显优于最新技术。结果还表明，结合转移和多任务学习使MTTLADE在解决多头问题和提取复杂ADE方面更加有效。在TAC 2017和n2c2 2018共享任务提供的两个数据集上进行的实验评估显示了MTTLADE的有效性和通用性。拟议的MTTLADE系统在两个数据集上均明显优于最新技术。结果还表明，结合转移和多任务学习使MTTLADE在解决多头问题和提取复杂ADE方面更加有效。在TAC 2017和n2c2 2018共享任务提供的两个数据集上进行的实验评估显示了MTTLADE的有效性和通用性。拟议的MTTLADE系统在两个数据集上均明显优于最新技术。结果还表明，结合转移和多任务学习使MTTLADE在解决多头问题和提取复杂ADE方面更加有效。