UNCC Biomedical Semantic Question Answering Systems. BioASQ: Task7B, PhaseB In this paper, we detail our submission to the 2019, 7th year, BioASQ competition. We present our approach for Task7b, Phase B, Exact Answering Task. These Question Answering (QA) tasks include Factoid, YesNo, List Type Question answering. Our system is based on a contextual word embedding model. We have used a Bidirectional Encoder Representations from Transformers(BERT) based system, fined tuned for biomedical question answering task using BioBERT. In the third test batch set, our system achieved the highest MRR score for Factoid Question Answering task. Also, for List type question answering task our system achieved the highest recall score in the fourth test batch set. Along with our detailed approach, we present the results for our submissions, and also highlight identified downsides for our current approach and ways to improve them in our future experiments.

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.