Abstract
Slot filling and intent detection are two important tasks in a spoken language understanding (SLU) system, it is becoming a tendency that two tasks are jointing learn in SLU. However, many existing model only conduct join model by share parameters on the surface level rather than bi-directional interaction for slot filling and intent detection tasks. In this paper, we designed a dual interaction model based on the gate mechanism. First, We utilize a Dilated Convolutional Neural Networks (DCNN) block with self-attention to better capture the semantic of utterance. Besides, for the two tasks we adopt gate mechanism to get the interaction information of intent and slot, which can control the passing rate and make fully use of semantic relevance between slot filling and intent detection. Finally, the experiments results show that our model has significantly improved in the slot filling F1, intent detection accuracy on the ATIS and SNIPS datasets and overmatch other prior methods.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China under grant nos. 61702305, 11971270, and 61903089, the China Postdoctoral Science Foundation under grant no. 2017M622234 and Science and Technology Support Plan of Youth Innovation Team of Shandong higher School under grant no. 2019KJN2014.
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Sun, C., Lv, L., Liu, T. et al. A joint model based on interactive gate mechanism for spoken language understanding. Appl Intell 52, 6057–6064 (2022). https://doi.org/10.1007/s10489-021-02544-7
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DOI: https://doi.org/10.1007/s10489-021-02544-7