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• W2894887961 abstract "In this paper, we proposed an attention-based recurrent neural network model based on a tagging strategy for intent detection and word slot extraction. Unlike other joint models dividing the joint task into two sub-models by sharing parameters, we explore a tagging strategy to incorporate the intent detection task and word slot extraction task in a sequence labeling model. We implemented experiments on a public dataset and the results show that the tagging strategy methods outperform most of the existing pipelined and joint methods. Our tagging strategy model obtained 97.65% accuracy rate on intent detection task and 95.15% F1 score on word slot extraction task." @default.
• W2894887961 created "2018-10-12" @default.
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• W2894887961 date "2018-01-01" @default.
• W2894887961 modified "2023-09-26" @default.
• W2894887961 title "Attention-Based RNN Model for Joint Extraction of Intent and Word Slot Based on a Tagging Strategy" @default.
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• W2894887961 doi "https://doi.org/10.1007/978-3-030-01424-7_18" @default.
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