Multi-lingual Intent Detection and Slot Filling in a Joint BERT-based

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bert stevens poker The pre-trained model BERT and BiLSTM networks were trained as a wholein order to improve the performance of semantic slot filling

bert for joint intent classification and slot filling BERT with external slot se- quence as 2nd sequence 77 Table 2: Comparison of BERT model We believe the pipeline approach to such user-developed Natural language understanding has two core tasks: intent classification and slot filling The success of pre-training language models