A Character-Word Information Interaction Framework for Natural Language Understanding in Chinese Medical Dialogue Domain

Natural language understanding is a foundational task in medical dialogue systems. However, there are still two key problems to be solved: (1) Multiple meanings of a word lead to ambiguity of intent; (2) character errors make slot entity extraction difficult. To solve the above problems, this paper proposes a character-word information interaction framework (CWIIF) for natural language understanding in the Chinese medical dialogue domain. The CWIIF framework contains an intent information adapter to solve the problem of intent ambiguity caused by multiple meanings of words in the intent detection task and a slot label extractor to solve the problem of difficulty in yellowslot entity extraction due to character errors in the slot filling task. The proposed framework is validated on two publicly available datasets, the Intelligent Medical Consultation System (IMCS-21) and Chinese Artificial Intelligence Speakers (CAIS). Experimental results from both datasets demonstrate that the proposed framework outperforms other baseline methods in handling Chinese medical dialogues. Notably, on the IMCS-21 dataset, precision improved by 2.42%, recall by 3.01%, and the F1 score by 2.4%.