CliniQG4QA: Generating Diverse Questions for Domain Adaptation of Clinical Question Answering
CliniQG4QA: Generating Diverse Questions for Domain Adaptation of Clinical Question Answering
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DOI：
10.1109/bibm52615.2021.9669300
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发表时间：
2020-10
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通讯作者：
Xiang Yue;Xinliang Frederick Zhang;Ziyu Yao;Simon M. Lin;Huan Sun
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作者：
Xiang Yue;Xinliang Frederick Zhang;Ziyu Yao;Simon M. Lin;Huan Sun
临床问答（QA）旨在根据临床文本自动回答医学专业人员提出的问题。研究表明，在一个语料库上训练的神经QA模型可能不能很好地推广到来自不同研究所或不同患者组的新临床文本，在这些地方，大规模的QA对不容易用于模型再训练。为了应对这一挑战，我们提出了一个简单而有效的框架，CliniQG4QA，它利用问题生成（QG）在新的临床环境中合成QA对，并在不需要手动注释的情况下增强QA模型。为了生成训练QA模型所必需的不同类型的问题，我们进一步引入了一个基于seq2seq的问题短语预测（QPP）模块，该模块可以与大多数现有的QG模型一起使用，以多样化生成。我们的综合实验结果表明，由我们的框架生成的QA语料库可以在新的上下文上改进QA模型（就精确匹配而言，绝对增益高达8%），并且QPP模块在实现增益方面起着至关重要的作用。我们的数据集和代码可在https://github.com/sunlabosu/CliniQG4QA/上获得。
Clinical question answering (QA) aims to automatically answer questions from medical professionals based on clinical texts. Studies show that neural QA models trained on one corpus may not generalize well to new clinical texts from a different institute or a different patient group, where largescale QA pairs are not readily available for model retraining. To address this challenge, we propose a simple yet effective framework, CliniQG4QA, which leverages question generation (QG) to synthesize QA pairs on new clinical contexts and boosts QA models without requiring manual annotations. In order to generate diverse types of questions that are essential for training QA models, we further introduce a seq2seq-based question phrase prediction (QPP) module that can be used together with most existing QG models to diversify the generation. Our comprehensive experiment results show that the QA corpus generated by our framework can improve QA models on the new contexts (up to 8% absolute gain in terms of Exact Match), and that the QPP module plays a crucial role in achieving the gain.11Our dataset and code are available at: https://github.com/sunlabosu/CliniQG4QA/.