Abstract
With the development of medical technology, Chinese medical resources are extremely scarce. At this necessary time, the development of dialogue agents to interact with patients and provide clinical advice has attracted more and more attention. In the task of generative medical dialogue, the end-to-end method is often used to establish the model. However, traditional end-to-end models often generate deficient relevance to medical dialogue. Towards this end, we propose to integrate medical information into initial pre-trained model and use the division of sentence based on “words and expressions” to improve the accuracy of medical entity recall, which will make the model have a deeper understanding of medical field. Finally, we use the Chinese medical dialogue MedDG [1] to fine-tune the model, so that the model can give the reply to the doctor’s clinical inquiry for the disease content from the patient. The experimental results show that our framework achieves higher accuracy in disease diagnosis, which won the fourth place in the 2021 medical dialogue generation task containing Chinese.
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References
Liu, W., Tang, J., Qin, J., Xu, L., Li, Z., Liang, X.: MedDG: a large-scale medical consultation dataset for building medical dialogue system. arXiv preprint arXiv:2010.07497 (2020)
Li, X., Chen, Y.-N., Li, L., Gao, J.: End-to-end task-completion neural dialogue systems. arXiv preprint arXiv:1703.01008 (2018)
Huang, Z., Xu, W., Yu, K.: Bidirectional LSTM-CRF models for sequence tagging. arXiv preprint arXiv:1508.01991 (2015)
Na, D., Hao, F., Xu, C.: Named entity recognition of traditional chinese medicine patents based on BiLSTM-CRF. Wirel. Commun. Mob. Comput. 2021(2), 1–12 (2021)
Zeng, G., Yang, W., Ju, Z., Yang, Y.: Meddialog: a large-scale medical dialogue dataset. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, pp. 9241–9250 (2020)
Xu, L., Zhou, Q., Gong, K., Liang, X., Tang, J., Lin, L.: End-to-end knowledge routed relational dialogue system for automatic diagnosis. arXiv preprint arXiv:1901.10623 (2019)
Shivade, C., Raghavan, P., Fosler-Lussier, E., Embi, P.J.: A review of approaches to identifying patient phenotype cohorts using electronic health records. J. Am. Med. Inf. Assoc. 21(2), 221–230 (2014)
Liu, Q., Wei, Z., Peng, B., Dai, X.: Task-oriented dialogue system for automatic diagnosis. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pp. 201–207 (2018)
Liao, K., Liu, Q., Wei, Z., Peng, B., Chen, Q.: Task-oriented dialogue system for automatic disease diagnosis via hierarchical reinforcement learning. arXiv preprint: arXiv:2004.14254 (2020)
Budzianowski, P., Vulic, I.: Hello, it’s gpt-2-how can i help you? towards the use of pre-trained language models for task-oriented dialogue systems. arXiv preprint: arXiv:1907.05774 (2019)
Papineni, K., Roukos, S., Ward, T.: BLEU: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002)
Liu, Z., Wang, H., Niu, Z.-Y., Wu, H., Che, W., Liu, T.: Towards conversational recommendation over multi-type dialogs. arXiv preprint: arXiv:2005.03954 (2020)
Acknowledgments
This work was supported by the National Natural Science Foundation of China (Grant No. 61976156, No. 11803022 and No. 61702367), Tianjin Science and Technology Commissioner project (No. 20YDTPJC00560), the Natural Science Foundation of Tianjin (Grant No. 19JCYBJC15300), and the Research Project of Tianjin Municipal Commission of Education (No. 2018KJ105 and No. 2018KJ106).
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Wang, Y., Li, Z., Zeng, L., Zhao, T. (2022). End-to-End Pre-trained Dialogue System for Automatic Diagnosis. In: Qin, B., Wang, H., Liu, M., Zhang, J. (eds) CCKS 2021 - Evaluation Track. CCKS 2021. Communications in Computer and Information Science, vol 1553. Springer, Singapore. https://doi.org/10.1007/978-981-19-0713-5_10
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DOI: https://doi.org/10.1007/978-981-19-0713-5_10
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