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 con-text 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.- Anthology ID:
- 2020.coling-main.161
- Volume:
- Proceedings of the 28th International Conference on Computational Linguistics
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona, Spain (Online)
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 1795–1801
- Language:
- URL:
- https://aclanthology.org/2020.coling-main.161
- DOI:
- 10.18653/v1/2020.coling-main.161
- Cite (ACL):
- Shweta Yadav, Vishal Pallagani, and Amit Sheth. 2020. Medical Knowledge-enriched Textual Entailment Framework. In Proceedings of the 28th International Conference on Computational Linguistics, pages 1795–1801, Barcelona, Spain (Online). International Committee on Computational Linguistics.
- Cite (Informal):
- Medical Knowledge-enriched Textual Entailment Framework (Yadav et al., COLING 2020)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.coling-main.161.pdf