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.

翻译：问答是一项利用大规模文档集合回答事实性问题（factoid questions）的任务，旨在以自然语言响应用户查询并提供精确答案。问答系统依赖于高效的段落检索来选择候选上下文，其中传统稀疏向量空间模型（如TF-IDF或BM25）是该领域的标准方法。在互联网上，没有任何单一文章能涵盖用户问题可能涉及的所有答案。现有的稠密段落检索（Dense Passage Retrieval）模型基于2018年12月20日的Wikipedia快照作为源文档进行训练。问答系统（QA）通过利用大规模标注数据集构建的开域与机器阅读理解系统已取得显著进展。然而在临床领域，该问题仍相对未被充分探索。多项调查表明，生物医学问题无法从Wikipedia文章中获得准确答案。在本工作中，我们针对生物医学领域改进了现有DPR框架，并从可靠医疗答案来源PubMed文章中检索答案。在BioASQ QA数据集上的评估结果显示，我们微调后的稠密检索器取得了0.81的F1分数。