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.
Preprint version Proceedings of the 28th International Conference on Computational Linguistics, 2020.
This work by Shweta Yadav, Vishal Pallagani, and Amit Sheth is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Yadav, S., Pallagani, V., & Sheth, A. (2020). Medical Knowledge-enriched Textual Entailment Framework. ArXiv:2011.05257 [Cs]. http://arxiv.org/abs/2011.05257