  One of the cardinal tasks in achieving robust medical question answering
systems is textual entailment. The existing approaches make use of an ensemble
of pre-trained language models or data augmentation, often to clock higher
numbers on the validation metrics. However, two major shortcomings impede
higher success in identifying entailment: (1) understanding the focus/intent of
the question and (2) ability to utilize the real-world background knowledge to
capture the context beyond the sentence. In this paper, we present a novel
Medical Knowledge-Enriched Textual Entailment framework that allows the model
to acquire a semantic and global representation of the input medical text with
the help of a relevant domain-specific knowledge graph. We evaluate our
framework on the benchmark MEDIQA-RQE dataset and manifest that the use of
knowledge enriched dual-encoding mechanism help in achieving an absolute
improvement of 8.27% over SOTA language models. We have made the source code
available here.
