BERT-based joint intent detection and slot filling with intent-slot attention mechanism (INTERSPEECH 2021)
Details of our JointIDSF model architecture, dataset construction and experimental results can be found in our following paper:
@inproceedings{JointIDSF,
title = {{Intent Detection and Slot Filling for Vietnamese}},
author = {Mai Hoang Dao and Thinh Hung Truong and Dat Quoc Nguyen},
booktitle = {Proceedings of the 22nd Annual Conference of the International Speech Communication Association (INTERSPEECH)},
year = {2021}
}
Please CITE our paper whenever our dataset or model implementation is used to help produce published results or incorporated into other software.
By downloading our dataset, USER agrees:
- to use the dataset for research or educational purposes only.
- to not distribute the dataset or part of the dataset in any original or modified form.
- and to cite our paper above whenever the dataset is employed to help produce published results.
git clone https://github.com/VinAIResearch/JointIDSF.git
cd JointIDSF/
pip3 install -r requirements.txt
Run the following two bash files to reproduce results presented in our paper:
./run_jointIDSF_PhoBERTencoder.sh
./run_jointIDSF_XLM-Rencoder.sh
run_jointIDSF_XLM-Rencoder.sh
can adapt for other languages that have gold annotated corpora available for intent detection and slot filling. Please prepare your data with the same format as in the data
directory.We also provide model checkpoints of JointBERT+CRF and JointIDSF. Please download these checkpoints if you want to make inference on a new text file without training the models from scratch.
http://public.vinai.io/JointIDSF_PhoBERTencoder.tar.gz
http://public.vinai.io/JointIDSF_XLM-Rencoder.tar.gz
http://public.vinai.io/JointBERT-CRF_PhoBERTencoder.tar.gz
http://public.vinai.io/JointBERT-CRF_XLM-Rencoder.tar.gz
Example of tagging a new text file using JointIDSF model:
python3 predict.py --input_file <path_to_input_file> \
--output_file <output_file_name> \
--model_dir JointIDSF_XLM-Rencoder
where the input file is a raw text file (one utterance per line).
Our code is based on the unofficial implementation of the JointBERT+CRF paper from https://github.com/monologg/JointBERT