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RNN-for-Joint-NLU

Pytorch implementation of "Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling" (https://arxiv.org/pdf/1609.01454.pdf)

<img src="https://github.com/DSKSD/RNN-for-Joint-NLU/raw/master/images/jointnlu0.png"/>

Intent prediction and slot filling are performed in two branches based on Encoder-Decoder model.

dataset (Atis)

You can get data from <a href="https://github.com/yvchen/JointSLU/tree/master/data ">here</a>

Requirements

Train

python3 train.py --data_path 'your data path e.g. ./data/atis-2.train.w-intent.iob'

Result

<img src="https://github.com/DSKSD/RNN-for-Joint-NLU/raw/master/images/jointnlu1.png"/> <img src="https://github.com/DSKSD/RNN-for-Joint-NLU/raw/master/images/jointnlu2.png"/> <img src="https://github.com/DSKSD/RNN-for-Joint-NLU/raw/master/images/jointnlu3.png"/>