Question answering is a task that answers factoid questions using a large
collection of documents. It aims to provide precise answers in response to the
user's questions in natural language. Question answering relies on efficient
passage retrieval to select candidate contexts, where traditional sparse vector
space models, such as TF-IDF or BM25, are the de facto method. On the web,
there is no single article that could provide all the possible answers
available on the internet to the question of the problem asked by the user. The
existing Dense Passage Retrieval model has been trained on Wikipedia dump from
Dec. 20, 2018, as the source documents for answering questions. Question
answering (QA) has made big strides with several open-domain and machine
comprehension systems built using large-scale annotated datasets. However, in
the clinical domain, this problem remains relatively unexplored. According to
multiple surveys, Biomedical Questions cannot be answered correctly from
Wikipedia Articles. In this work, we work on the existing DPR framework for the
biomedical domain and retrieve answers from the Pubmed articles which is a
reliable source to answer medical questions. When evaluated on a BioASQ QA
dataset, our fine-tuned dense retriever results in a 0.81 F1 score.

我们在临床领域中通过对现有 DPR 框架的改进，从可靠的 Pubmed 文章中检索答案，使其在 BioASQ QA 数据集上的评估结果为 0.81 的 F1 得分。