Medical Knowledge-enriched Textual Entailment Framework

• URL: http://arxiv.org/abs/2011.05257v1
• Date: Tue, 10 Nov 2020 17:25:27 GMT
• Title: Medical Knowledge-enriched Textual Entailment Framework
• Authors: Shweta Yadav, Vishal Pallagani, Amit Sheth
• Abstract summary: We present a novel Medical Knowledge-Enriched Textual Entailment framework. 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.
• Score: 5.493804101940195
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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