This is a code for our paper Multi-Classification Model for Spoken Language Understanding published in the 1st Chinese Audio-Textual Spoken Language Understanding Challenge (CATSLU), ICMI 2019.
The dataset can be downloaded from https://drive.google.com/file/d/1Wpzfq_qbUJm2ddjUO9oNYBAG83MJZxf2/view?usp=sharing.
The evaluation result on test set is shown as bellow.
-
python 3
-
requirements.txt
Get dataset and put it into folder data
.
data
├── map
│ ├── audios
│ ├── development.json
│ ├── lexicon
│ ├── ontology.json
│ ├── test_unlabelled.json
│ └── train.json
├── music
│ ├──....
├── video
│ ├──....
└── weather
├──....
Just run the shell scripts
bash run.sh
Assume the test file name is test_unlabelled.json
.
If the data_dir
is ./data/music
, we have
python tagNet/infer.py --task_name=music --data_dir=./data/music --output_dir=./experiment/bert/music
and the result will write in ./experiment/bert/write_results.json
.
Note that output
dir includes config.json
、pytorch_model.bin
、vocab.txt
.
@inproceedings{Tan_2019_Multi,
author = {Tan, Chaohong and Ling, Zhenhua},
title = {Multi-Classification Model for Spoken Language Understanding},
year = {2019},
url = {https://doi.org/10.1145/3340555.3356099},
booktitle = {2019 International Conference on Multimodal Interaction},
pages = {526–530}
}