Question Answering based Clinical Text Structuring Using Pretrained Language Model Clinical text structuring is a critical and fundamental task for clinical research. Traditional methods such as taskspecific endtoend models and pipeline models usually suffer from the lack of dataset and error propagation. In this paper, we present a question answering based clinical text structuring (QACTS) task to unify different specific CTS tasks and make dataset shareable. A novel model that aims to introduce domainspecific features (e.g., clinical named entity information) into pretrained language model is also proposed for QACTS task. Experimental results on Chinese pathology reports collected from Ruijing Hospital demonstrate our presented QACTS task is very effective to improve the performance on specific tasks. Our proposed model also competes favorably with strong baseline models in specific tasks.

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.