@inproceedings{yadav-etal-2020-medical,
title = "Medical Knowledge-enriched Textual Entailment Framework",
author = "Yadav, Shweta and
Pallagani, Vishal and
Sheth, Amit",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.161",
doi = "10.18653/v1/2020.coling-main.161",
pages = "1795--1801",
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.",
}
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%0 Conference Proceedings
%T Medical Knowledge-enriched Textual Entailment Framework
%A Yadav, Shweta
%A Pallagani, Vishal
%A Sheth, Amit
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F yadav-etal-2020-medical
%X 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.
%R 10.18653/v1/2020.coling-main.161
%U https://aclanthology.org/2020.coling-main.161
%U https://doi.org/10.18653/v1/2020.coling-main.161
%P 1795-1801
Markdown (Informal)
[Medical Knowledge-enriched Textual Entailment Framework](https://aclanthology.org/2020.coling-main.161) (Yadav et al., COLING 2020)
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.