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.
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Acknowledgement
This work was supported by the National Key Research and Development program of China (No. 2018YFB1004703).
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Zhang, D., Fang, Z., Cao, Y., Liu, Y., Chen, X., Tan, J. (2018). Attention-Based RNN Model for Joint Extraction of Intent and Word Slot Based on a Tagging Strategy. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11141. Springer, Cham. https://doi.org/10.1007/978-3-030-01424-7_18
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