# DATLMedQA: A Data Augmentation and Transfer Learning Based Solution for Medical Question Answering
## 2 Related Work  
### Medical Question Answering and Data Augmentation  
The main issues faced by medical question answering tasks are focused on insufficient samples [22] and medical terminologies [23]. To address those issues, various transfer learning and augmentation models have been proposed to improve the prediction performance of the system [24; 25]. Semantic biomedical question answering is an important task in the application of biomedical question answering [26; 27]. Due to the reliability of answers it can provide, it has attracted widespread attention. In a question answering system, a better word representation is very important, and proper word embeddings can usually considerably improve the performance of the system. With the successful application of pretraining models in general natural language processing tasks, pretraining models have also been widely used in the field of biomedicine. Pretraining models have demonstrated their effectiveness in biomedical question answering tasks [28]. In addition to proper word embedding, named entity recognition is also important in biomedical question answering. Inspired by transfer learning, [PERSON] et al. developed a model to fine-tune BioBERT with a named entity dataset to improve the performance of question answering. In addition, the model uses BiLSTM to encode the question text to obtain sentence-level information. The model also uses bagging to further improve its overall performance, which better combines question-level and token-level information. The model has been evaluated on BioASQ 6b and 7b datasets, and the results demonstrate its advantages and promising potentials [29]. When adapting to specialized domains, such as the COVID-19 literature, model fine-tuning and pretraining can be costly. In order to improve their domain adaptation, [PERSON] et al. proposed an approach called biomedical entity-aware masking (BEM), which allowed masked language models to learn entity-centric knowledge based on pivotal entities at hand, and used these entities to drive the fine-tuning of the language model (LM). The performance of this model on several biomedical quality assurance datasets was comparable to state-of-the-art models [30]. Healthcare has attracted significant attention, especially during the pandemic. In order to seek healthcare information, tons of questions have appeared on the Internet, which makes it even more urgent to develop an efficient and reliable question answering system. However, people often provide unnecessary information in their questions, such as a patient's medical history, demographic information, etc. It adds the challenges of understanding natural language questions. In addition, it is crucial to provide accurate and relevant answers instead of paragraphs or even documents. To achieve a reliable medical question answering system, the main tasks include question summarization, as well as multi-answer summarization. The MEDIQA 2021 challenge tackled three summarization tasks in the medical domain: consumer health question summarization, multi-answer summarization, and radiology report summarization. [PERSON] et al. (NLM at MEDIQA 2021) proposed an approach that first pretrained transformer models on a task-specific summarization dataset and introduced a transfer learning method for question summarization and multi-answer summarization tasks, and then fine-tuned the model for these two tasks by incorporating medical entities. Their approach won the second, sixth and fourth place for the question summarization task in ROUGE-1 (the overlap of unigram (each word) between the system and the reference summaries), ROUGE-2 (the overlap of bigrams between the system and the reference summaries) and ROUGE-L (the longest common subsequence (LCS)-based statistics) scores, respectively, ref. [31]. Question generation (QG) can automatically generate questions from a given context [13], which can be used to build QA datasets. [PERSON] et al.