生物医学问答中融合领域知识的上下文嵌入和模型加权
Contextual embedding and model weighting by fusing domain knowledge on Biomedical Question Answering
摘要：
生物医学问答旨在从生物医学领域获得给定问题的答案。由于其对生物医学领域知识的要求很高，模型很难从有限的训练数据中学习领域知识。我们提出了一种上下文嵌入方法，结合开放领域的QA模型\aoa和\biobert模型预训练的生物医学领域的数据。我们采用无监督预训练的大型生物医学语料库和监督微调生物医学问答数据集。此外，我们采用一个基于MLP的模型加权层，自动利用两个模型的优点，提供正确的答案。利用PubMed语料库构建的公共数据集\biomrc对该方法进行了评价。实验结果表明，我们的模型优于国家的最先进的系统由一个很大的利润。
英文摘要：
Biomedical Question Answering aims to obtain an answer to the given question from the biomedical domain. Due to its high requirement of biomedical domain knowledge, it is difficult for the model to learn domain knowledge from limited training data. We propose a contextual embedding method that combines open-domain QA model \aoa and \biobert model pre-trained on biomedical domain data. We adopt unsupervised pre-training on large biomedical corpus and supervised fine-tuning on biomedical question answering dataset. Additionally, we adopt an MLP-based model weighting layer to automatically exploit the advantages of two models to provide the correct answer. The public dataset \biomrc constructed from PubMed corpus is used to evaluate our method. Experimental results show that our model outperforms state-of-the-art system by a large margin.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
