Incorporating Medical Knowledge to Transformer-based Language Models for Medical Dialogue Generation

Usman Naseem, Ajay Bandi, Shaina Raza, Junaid Rashid, Bharathi Raja Chakravarthi


Abstract
Medical dialogue systems have the potential to assist doctors in expanding access to medical care, improving the quality of patient experiences, and lowering medical expenses. The computational methods are still in their early stages and are not ready for widespread application despite their great potential. Existing transformer-based language models have shown promising results but lack domain-specific knowledge. However, to diagnose like doctors, an automatic medical diagnosis necessitates more stringent requirements for the rationality of the dialogue in the context of relevant knowledge. In this study, we propose a new method that addresses the challenges of medical dialogue generation by incorporating medical knowledge into transformer-based language models. We present a method that leverages an external medical knowledge graph and injects triples as domain knowledge into the utterances. Automatic and human evaluation on a publicly available dataset demonstrates that incorporating medical knowledge outperforms several state-of-the-art baseline methods.
Anthology ID:
2022.bionlp-1.10
Volume:
Proceedings of the 21st Workshop on Biomedical Language Processing
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venues:
ACL | BioNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
110–115
Language:
URL:
https://aclanthology.org/2022.bionlp-1.10
DOI:
10.18653/v1/2022.bionlp-1.10
Bibkey:
Cite (ACL):
Usman Naseem, Ajay Bandi, Shaina Raza, Junaid Rashid, and Bharathi Raja Chakravarthi. 2022. Incorporating Medical Knowledge to Transformer-based Language Models for Medical Dialogue Generation. In Proceedings of the 21st Workshop on Biomedical Language Processing, pages 110–115, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Incorporating Medical Knowledge to Transformer-based Language Models for Medical Dialogue Generation (Naseem et al., BioNLP 2022)
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PDF:
https://aclanthology.org/2022.bionlp-1.10.pdf