Jonze

Joint Slot and Intent Extraction implementation in Tensorflow2.0

Contains restructured code from the following repo: https://github.com/shubham8111/Joint-NLU

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/

Usage

To install package:

pip install jonze

To train model:

from jonze import train  
train(dataset = "joint-nlu", datasets_root = "dataset", models_root = "model", layer_size=12)

To test model:

from jonze import test  
test(dataset = "joint-nlu", datasets_root = "dataset", models_root = "model", layer_size=12, batch_size=46)

Results

Snips Dataset:

Atis Dataset:

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.