PunCantonese: A Benchmark Corpus for Low-Resource Cantonese Punctuation Restoration from Speech Transcripts

This repository contains the data and source code of the paper accepted at the InterSpeech2023, PunCantonese: A Benchmark Corpus for Low-Resource Cantonese Punctuation Restoration from Speech Transcripts.

Data

The dataset would be available upon request to this email address: yunxiang.li@link.cuhk.edu.hk

Model

We propose a Transformer-based neural network model to evaluate the PunCantonese corpus.

The model exploits pre-trained language models to obtain a good network initialization, a multi-task learning objective to prevent the network from paying too much attention to the largest subset of written-style sentences and a novel Jyutping embedding layer to represent a Cantonese character with its Jyutping sequence which potentially enables the model to incorporate phonetic features that are not explicitly available in Cantonese characters. Then we have one Bi-LSTM and one linear layer for classification.

Training

To train our model with default settings and with both multitask learning and jyutping embedding on, run the following command

python src/train_jyupin_multi.py --cuda=True --pretrained-model=bert-base-multilingual-uncased --freeze-bert=False --lstm-dim=-1 --seed=0 --lr=2e-5 --epoch=15 --use-crf=False --data-path=data --save-path=out --batch-size=32 --sequence-length=128 --loss=focal --multitask=True --jyutping=True

Acknowledgements

This project is based on modifications made to the original code from Punctuation Restoration using Transformer Models. Special thanks to the authors from this work for providing the excellent code base.

License

This repository is under the Apache License 2.0.