A Prompt Learning Based Intent Recognition Method on a Chinese Implicit Intent Dataset CIID


Computer Science

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As one of the core modules of the dialogue system, intent recognition plays an important role in human–computer interaction. Most of the existing intent recognition research is limited to simple, direct, and explicit intent recognitions. However, the natural human–computer interactions are flexible and diverse, and the expressions are often the euphemistic implicit intentions. Therefore, the implicit intent recognition brings new research challenges in this field. This paper pioneers a Chinese Implicit Intent Dataset (CIID), which covers seven common intents from different fields, and the data is the text containing the user’s implicit intent. Based on this corpus, it is the first-time the prompt learning is employed for implicit intent recognition and by constructing a suitable prompt template so that the model can get “relevant hints” to dig out the true intention of the user. We also evaluate a range of classification models on CIID dataset in this study. Experimental results show that our proposed model achieves the state-of-the-art recognition accuracy. Furthermore, we explore the performance of the model with few-shot settings, the results also prove that the proposed method is advanced and robust.

Journal Title

Neural Processing Letters

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Digital Object Identifier (DOI)