Joint Slot and Intent Extraction implementation in Tensorflow2.0
Implementation of Bi-LSTM based NLU baseline and SlotGated-SLU (Goo et al, 2018)(https://www.csie.ntu.edu.tw/~yvchen/doc/NAACL18_SlotGated.pdf) Models are evaulated on Snips and ATIS datasets.
Experiments did not reproduce improvements by SlotGated model over Basline model, on snips dataset.
Preprocessing modules reused from following repo: https://github.com/MiuLab/SlotGated-SLU/
| Model | Slot F1 | Intent accuracy | Semantic Accuracy |
|---|---|---|---|
| Baseline | 84.30 | 96.57 | 66.43 |
| Slot Gated | 83.5 | 95.57 | 66.85 |
| Model | Slot F1 | Intent accuracy | Semantic Accuracy |
|---|---|---|---|
| Baseline | 95.08 | 94.62 | 81.97 |
| Slot Gated | 94.57 | 96.41 | 83.65 |
P.S. Sometimes Slot F1 might get stuck at zero during training, better weight intialization or training a few epochs only on slot loss can resolve the issue.