Dual Model Weighting Strategy and Data Augmentation in Biomedical Question Answering

Yongping Du, Jingya Yan, Yiliang Zhao, Yuxuan Lu, Xingnan Jin

Abstract: Biomedical Question Answering aims to extract an answer to the given question from a biomedical context. Due to the strong professionalism of specific domain, it’s more difficult to build large-scale datasets for specific domain question answering. Existing methods are limited by the lack of training data, and the performance is not as good as in open-domain settings. We propose a model weighting strategy for the final answer prediction in biomedical domain, which combines the advantage of two models, open-domain model QANet and BioBERT pretrained in biomedical domain data. Especially, we adopt effective data augmentation strategies to improve the model performance, including round-trip translation and summarization. The public biomedical dataset collected from PubMed provided by BioASQ is used to evaluate our approach. The results show that the model performance has been improved significantly on BioASQ 6B, 7B and 8B datasets compared to the single model.