Hierarchical Question-Aware Context Learning with Augmented Data for Biomedical Question Answering

This paper is concerned with the task of biomedical Question Answering (QA) which refers to extracting an answer to the given question from a biomedical context. Current works have made progress on this task, but they are still severely restricted by the insufficient training data due to the domain-specific nature, which motivates us to further explore a powerful way to solve this problem. We propose a Hierarchical Question-Aware Context Learning (HQACL) model for the biomedical QA task constituted by multi-level attention. The interaction between the question and the context can be captured layer by layer, with multi-grained embeddings to strengthen the ability of the language representation. A special training method called DA, including two parts namely domain adaptation and data augmentation, is also introduced to enhance the model performance. Domain adaptation can be defined as pre-training on a large-scale open-domain dataset and fine-tuning on the small training set of the target domain. As for the data augmentation, the Round-trip translation method is adopted to create new data with various expressions, which almost doubles the training set. The public biomedical dataset collected from PubMed provided by BioASQ is used to evaluate our model. The results show that our approach is superior to the best recent solution and achieves a new state of the art.