Papers with attention-based neural network model

1 papers
Joint Multiple Intent Detection and Slot Labeling for Goal-Oriented Dialog (N19-1)

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Challenge: Neural network models have gained traction for sentence-level intent classification and token-based slot-label identification.
Approach: They propose a neural network model that performs multi-label classification for identifying multiple intents and produces token-based slot-l labels at the token-level.
Outcome: The proposed model provides a small but statistically significant improvement on the ATIS dataset and 55% accuracy improvement on an internal multi-intent dataset.

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