Journal of Shandong University(Engineering Science) ›› 2020, Vol. 50 ›› Issue (6): 68-75.doi: 10.6040/j.issn.1672-3961.0.2020.236

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Pre-trained based joint model for intent classification and slot filling in Chinese spoken language understanding

MA Changxia1, ZHANG Chen2   

  1. 1. School of Computer Engineering, Jiangsu Ocean University, Lianyungang 222005, Jiangsu, China;
    2. Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
  • Published:2020-12-15

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Abstract: We explored a joint model for intent classification and slot filling based on pre-train and attention mechanism because intent classification and slot filling were correlative. We combined bidirectional long short-term memory(BiLSTM), conditional random fields(CRF)and bidirectional encoder representations from transformers(BERT), which supported bidirectional and self-attentional mechanism without relying heavily on hand-crafted features and domain-specific knowledge or resources, into the proposed model. We compared the performance of our proposed architecture with the state-of-the-art models. Experiments on dataset demonstrated that the proposed architecture outperformed the state-of-the-art approaches on both tasks. Furthermore, we presented a new dialogue corpus from autonomous bus information inquiry system(ABIIS), and our methods yielded effective improvement on intent classification accuracy and slot filling F1 compared with a state-of-the-art baseline.

Key words: intent classification, slot filling, pre-train, bidirectional encoder representation, multi-head attention

CLC Number: 

  • TP391
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