@inproceedings{mao-etal-2022-hierarchical,
title = "Hierarchical Representation-based Dynamic Reasoning Network for Biomedical Question Answering",
author = "Mao, Jianguo and
Zhang, Jiyuan and
Zeng, Zengfeng and
Peng, Weihua and
Jiang, Wenbin and
Wang, Xiangdong and
Liu, Hong and
Lyu, Yajuan",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.127",
pages = "1480--1489",
abstract = "Recently, Biomedical Question Answering (BQA) has attracted growing attention due to its application value and technical challenges. Most existing works treat it as a semantic matching task that predicts answers by computing confidence among questions, options and evidence sentences, which is insufficient for scenarios that require complex reasoning based on a deep understanding of biomedical evidences. We propose a novel model termed Hierarchical Representation-based Dynamic Reasoning Network (HDRN) to tackle this problem. It first constructs the hierarchical representations for biomedical evidences to learn semantics within and among evidences. It then performs dynamic reasoning based on the hierarchical representations of evidences to solve complex biomedical problems. Against the existing state-of-the-art model, the proposed model significantly improves more than 4.5{\%}, 3{\%} and 1.3{\%} on three mainstream BQA datasets, PubMedQA, MedQA-USMLE and NLPEC. The ablation study demonstrates the superiority of each improvement of our model. The code will be released after the paper is published.",
}
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<abstract>Recently, Biomedical Question Answering (BQA) has attracted growing attention due to its application value and technical challenges. Most existing works treat it as a semantic matching task that predicts answers by computing confidence among questions, options and evidence sentences, which is insufficient for scenarios that require complex reasoning based on a deep understanding of biomedical evidences. We propose a novel model termed Hierarchical Representation-based Dynamic Reasoning Network (HDRN) to tackle this problem. It first constructs the hierarchical representations for biomedical evidences to learn semantics within and among evidences. It then performs dynamic reasoning based on the hierarchical representations of evidences to solve complex biomedical problems. Against the existing state-of-the-art model, the proposed model significantly improves more than 4.5%, 3% and 1.3% on three mainstream BQA datasets, PubMedQA, MedQA-USMLE and NLPEC. The ablation study demonstrates the superiority of each improvement of our model. The code will be released after the paper is published.</abstract>
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%0 Conference Proceedings
%T Hierarchical Representation-based Dynamic Reasoning Network for Biomedical Question Answering
%A Mao, Jianguo
%A Zhang, Jiyuan
%A Zeng, Zengfeng
%A Peng, Weihua
%A Jiang, Wenbin
%A Wang, Xiangdong
%A Liu, Hong
%A Lyu, Yajuan
%S Proceedings of the 29th International Conference on Computational Linguistics
%D 2022
%8 October
%I International Committee on Computational Linguistics
%C Gyeongju, Republic of Korea
%F mao-etal-2022-hierarchical
%X Recently, Biomedical Question Answering (BQA) has attracted growing attention due to its application value and technical challenges. Most existing works treat it as a semantic matching task that predicts answers by computing confidence among questions, options and evidence sentences, which is insufficient for scenarios that require complex reasoning based on a deep understanding of biomedical evidences. We propose a novel model termed Hierarchical Representation-based Dynamic Reasoning Network (HDRN) to tackle this problem. It first constructs the hierarchical representations for biomedical evidences to learn semantics within and among evidences. It then performs dynamic reasoning based on the hierarchical representations of evidences to solve complex biomedical problems. Against the existing state-of-the-art model, the proposed model significantly improves more than 4.5%, 3% and 1.3% on three mainstream BQA datasets, PubMedQA, MedQA-USMLE and NLPEC. The ablation study demonstrates the superiority of each improvement of our model. The code will be released after the paper is published.
%U https://aclanthology.org/2022.coling-1.127
%P 1480-1489
Markdown (Informal)
[Hierarchical Representation-based Dynamic Reasoning Network for Biomedical Question Answering](https://aclanthology.org/2022.coling-1.127) (Mao et al., COLING 2022)
ACL
- Jianguo Mao, Jiyuan Zhang, Zengfeng Zeng, Weihua Peng, Wenbin Jiang, Xiangdong Wang, Hong Liu, and Yajuan Lyu. 2022. Hierarchical Representation-based Dynamic Reasoning Network for Biomedical Question Answering. In Proceedings of the 29th International Conference on Computational Linguistics, pages 1480–1489, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.