Papers with Slot filling and intent detection

2 papers
SlotRefine: A Fast Non-Autoregressive Model for Joint Intent Detection and Slot Filling (2020.emnlp-main)

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Challenge: Slot filling and intent detection are two main tasks in spoken language understanding systems.
Approach: They propose a non-autoregressive slot filling model with two-pass iteration mechanism to handle uncoordinated slots problem.
Outcome: The proposed model significantly outperforms previous models in slot filling task while speeding up decoding.
Syntactic Graph Convolutional Network for Spoken Language Understanding (2020.coling-main)

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Challenge: Existing work on slot filling and intent detection builds joint models without prior knowledge of linguistic knowledge.
Approach: They propose a joint model that integrates syntactic structure for learning slot filling and intent detection jointly.
Outcome: The proposed model outperforms existing models on two public benchmark datasets and further improves on slot filling and intent detection.

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