Recently, biomedical version of embeddings obtained from language models such as BioELMo have shown state-of-the-art results for the textual inference task in the medical domain. In this paper, we explore how to incorporate structured domain knowledge, available in the form of a knowledge graph (UMLS), for the Medical NLI task. Specifically, we experiment with fusing embeddings obtained from knowledge graph with the state-of-the-art approaches for NLI task (ESIM model). We also experiment with fusing the domain-specific sentiment information for the task. Experiments conducted on MedNLI dataset clearly show that this strategy improves the baseline BioELMo architecture for the Medical NLI task.

翻译：最近,从BioELMO等语言模型获得的生物医学嵌入版本展示了医学领域文字推断任务的最新结果。在本文中,我们探讨了如何将以知识图(UMLS)形式提供的结构化域知识纳入医学国家实验室任务。具体地说,我们试验了将从知识图获得的嵌入与国家实验室任务的最新方法(ESIMM模式)相结合。我们还试验了将特定域感知信息用于这一任务。在MedNLI数据集上进行的实验清楚地表明,这一战略改善了医学国家实验室任务的基线生物ELMO结构。