Intent detection and slot filling are two main tasks for building a spoken language understanding(SLU) system, the joint model of the two tasks is the research hotspot of the dialogue system. These two tasks are closely tied and the slots often highly depend on the intent. For the recent joint model: the fixed threshold is difficult to extract the positive votes in different domains. And the complex intent information guides the slot filling insufficiently. This paper proposed a fine-grained information integrated model for multiple intent detection and slot filling. Among this model, the intent information obtained by the intent decoder was concatenate with the encoding representation of each token to form an intent guided integrated encoding representation, so as to offer fine-grained intent information for the token-level slot prediction. At the same time, calculating the median of the maximum intent score and the minimum intent score to obtain the logic-adaptive threshold, and used it replace the fixed threshold. The logic-adaptive threshold can vary with the score distribution of different intent labels. Experimental results on two multi-label datasets verifies the performance of the proposed model.

[2023-03-30] Accepted Paper /doc/2023.01.0015/v1

[2023-09-05] Printed Article /doc/2023.01.0015/v2