Transfer Learning for Biomedical Question Answering-猫眼课题宝

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2020

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Arda Akdemir;T. Shibuya

。基于深度神经网络（DNN）的机器学习模型在许多研究领域取得了显着的成功。然而，最近的许多研究表明，这些方法在推广到未见过的例子和生物医学领域等新领域方面存在局限性。此外，基于监督学习的 DNN 模型需要大量标记数据，而这些数据对于生物医学问答任务等许多任务来说并不容易获得。事实证明，迁移学习可以通过从辅助任务传输信息来提高源任务的性能来缓解这些挑战，并且对于低资源任务特别有用。这些观察和发现促使我们研究迁移学习和多任务学习对生物医学问答任务的影响。我们提出了一种新颖的多任务学习模型来同时学习生物医学实体和问题。在这项工作中，我们解释了用于参加 BioASQ 8B 挑战的三种不同的神经模型。我们的初步结果表明，从生物医学实体识别任务中传输信息可以为生物医学问答任务带来改进。

. Deep Neural Network (DNN) based Machine Learning models achieved remarkable success in many ﬁelds of research. Yet, many recent studies show the limitations of these approaches to generalize to unseen examples and to new domains such as the biomedical domain. Besides, supervised-learning based DNN models require a substantial amount of labeled data which is not readily available for many tasks such as the biomedical question answering task. Transfer Learning is shown to mitigate these challenges by transferring information from auxiliary tasks to improve the performance on a source task, and shown to be especially useful for low-resource tasks. These observations and ﬁndings motivated us to investigate the eﬀect of transfer learning and multi-task learning on the biomedical question answering task. We proposed a novel multi-task learning model to learn biomedical entities and questions simultaneously. In this work, we explain the three diﬀerent neural models we used to participate for the BioASQ 8B challenge. Our initial results showed that transferring information from the biomedical entity recognition task brings improvement for the biomedical question answering task.

Transfer Learning for Biomedical Question Answering

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