Knowledge Abstraction Matching for Medical Question Answering Medical Question Answering (medical QA), which studies the problem of automatically answering patientsu0027 medical questions online, is one of the major applications of bioinformatics. Though many efforts have been made before, the medical QA system still deserves delicate algorithm optimization due to the serious application scenario and strict requirement for the answer quality. In this paper, we introduce a novel Knowledge Abstraction Matching (KAM) method for the medical QA problem. The intuition of KAM is that there are many frequent repeat text segments appearing in the answers across different questions. From this view, we propose a new method that consists of frequent segment  N gram mining, medical knowledge abstraction, medical segment matching and answer reretrieval. KAM has been incorporated into Baiduu0027s enterprise medical QA system MelodyQA deployed on the backend of Muzhi Doctor. The evaluation shows that the proposed method can generate more quality answers for MelodyQA with a significant improvement of question coverage under acceptable accuracy.

Hierarchical QuestionAware 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 domainspecific nature, which motivates us to further explore a powerful way to solve this problem. We propose a Hierarchical QuestionAware Context Learning (HQACL) model for the biomedical QA task constituted by multilevel attention. The interaction between the question and the context can be captured layer by layer, with multigrained 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 pretraining on a largescale opendomain dataset and finetuning on the small training set of the target domain. As for the data augmentation, the Roundtrip 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.