论文标题
CliniQG4QA：为临床问题的域适应产生不同的问题
CliniQG4QA: Generating Diverse Questions for Domain Adaptation of Clinical Question Answering
论文作者
论文摘要
临床问题回答（QA）旨在根据临床文本自动回答医学专业人员的问题。研究表明，在一个语料库中训练的神经质量检查模型可能无法很好地推广到来自不同研究所或其他患者组的新临床文本，在这些临床文本中，大规模的QA对不容易用于模型再培训。为了应对这一挑战，我们提出了一个简单而有效的框架CliniqG4QA，该框架利用问题产生（QG）在新的临床环境下合成QA对，并在不需要手动注释的情况下增强QA模型。为了生成对培训质量检查模型必不可少的各种类型的问题，我们进一步介绍了一个基于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，以使一代人多样化。我们的全面实验结果表明，我们的框架生成的质量检查可以改善新环境（确切匹配方面的绝对增益最高8％），并且QPP模块在获得增益方面起着至关重要的作用。
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 large-scale 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.
