CliniQG4QA: Generating Diverse Questions for Domain Adaptation of Clinical Question Answering
This addresses domain adaptation for clinical QA, enabling better generalization across institutes or patient groups without manual annotations, though it is incremental as it builds on existing QG methods.
The paper tackles the problem of clinical question answering models failing to generalize to new clinical contexts due to lack of annotated data, by proposing a framework that generates diverse QA pairs to adapt models, achieving up to 8% absolute gain in Exact Match.
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.