Papers with intent accuracy
A Bi-Model Based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling (N18-2)
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| Challenge: | Intent detection and slot filling are two main tasks for building a spoken language understanding system. |
| Approach: | They propose to use a sequence to sequence model to generate both intent and slot filling tasks together to perform the two tasks jointly. |
| Outcome: | The proposed model achieves 0.5% intent accuracy improvement and 0.9 % slot filling improvement on the ATIS benchmark data. |
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. |