MiuLab / Lattice-SLU

Source code for ASRU 2019 paper "Adapting Pretrained Transformer to Lattices for Spoken Language Understanding"

Adapting Pretrained Transformer to Lattices for Spoken Language Understanding

Paper

This repo contains source code of our ASRU 2019 paper "Adapting Pretrained Transformer to Lattices for Spoken Language Understanding"

Requirements

• Python >= 3.6

Required python packages are listed in requirements.txt.

Dataset

Unfortunately, we are not allowed to redistribute the dataset(ATIS). The dataset needs to be obtained from LDC

• https://catalog.ldc.upenn.edu/LDC93S5
• https://catalog.ldc.upenn.edu/LDC94S19
• https://catalog.ldc.upenn.edu/LDC95S26

Preprocess

Convert lattices to PLF format

We use the PLF format lattices, you can use this script to convert Kaldi lattices to PLF format

https://github.com/noisychannel/phrase_speech_translation/blob/master/asr_util/kaldi2FST.sh

Create dataset

python3 preproc-lattice.py [-h] dataset_file lattice_file out_file

• dataset_file: csv file with fields id, text, labels. The id field should match with the utterance ids.
• lattice_file: PLF lattice generated from the above script.
• out_file: output filename.

Training

Sample usage:

python3 run_openai_gpt_atis_lattice.py
    --train_dataset <train_csv_file>
    --eval_dataset <eval_csv_file>
    --model_name openai-gpt
    --output_dir <output_dir>
    --do_train --do_eval
    --task <intent/slot>
    --num_train_epochs 5
    --attn_bias
    --probabilistic_masks

• probabilistice_masks: Whether to use probabilistic_masks. Binary masks will be used if not set.
• linearize: linearize lattices.

Reference

Please cite the following paper

@inproceedings{
    huang2019adapting,
    title={Adapting Pretrained Transformer to Lattices for Spoken Language Understanding},
    author={Chao-Wei Huang and Yun-Nung Chen},
    booktitle={2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)},
    year={2019},
    organization={IEEE}
}