Prompt-based Generative Approach towards Multi-Hierarchical Medical Dialogue State Tracking
This addresses the problem of data scarcity and complex entity representation in medical dialogue systems for patients and healthcare applications, though it is incremental in adapting existing generative methods to a new domain.
The paper tackled the challenge of dialogue state tracking in medical dialogues by defining a multi-hierarchical state structure and proposing a prompt-based generative approach, which outperformed other methods and was effective in low-data scenarios.
The medical dialogue system is a promising application that can provide great convenience for patients. The dialogue state tracking (DST) module in the medical dialogue system which interprets utterances into the machine-readable structure for downstream tasks is particularly challenging. Firstly, the states need to be able to represent compound entities such as symptoms with their body part or diseases with degrees of severity to provide enough information for decision support. Secondly, these named entities in the utterance might be discontinuous and scattered across sentences and speakers. These also make it difficult to annotate a large corpus which is essential for most methods. Therefore, we first define a multi-hierarchical state structure. We annotate and publish a medical dialogue dataset in Chinese. To the best of our knowledge, there are no publicly available ones before. Then we propose a Prompt-based Generative Approach which can generate slot values with multi-hierarchies incrementally using a top-down approach. A dialogue style prompt is also supplemented to utilize the large unlabeled dialogue corpus to alleviate the data scarcity problem. The experiments show that our approach outperforms other DST methods and is rather effective in the scenario with little data.