UniPunc
Code Release
We release UniPunc model code under fairseq_code folder.
The code is implemented based on fairseq.
Data
You can download MUST-C data here, where we use release v1.0.
We also use mTEDx for construct English-mixed subset.
The data split is in data/ folder.
Case Study and Multilingual Performance
The case study instance and multilingual performance of our ICASSP submission. We provide those results for reviewers' convenience.
This repo does not contain an analysis, which is provided in section 5 of the paper.
We will release the code upon paper acceptance.
Case Study Instances
Please refer to case.tsv
Multilingual Performance
We also compare UniPunc and other baseline on multilingual sentences from mTEDx, where we select 6 languages, namely English, German, French, Spanish, Portuguese, Italian.
| Overall | Comma | Full Stop | Question Mark | |
|---|---|---|---|---|
| Att-GRU | 54.8 | 48.2 | 65.6 | 32.1 |
| BiLSTM | 53.6 | 46 | 65.2 | 30.1 |
| BERT | 74.7 | 71.5 | 80.1 | 61.1 |
| UniPunc-Mix | 75.4 | 72.1 | 80.8 | 71.3 |
Citation
https://ieeexplore.ieee.org/document/9747131
@INPROCEEDINGS{9747131,
author={Zhu, Yaoming and Wu, Liwei and Cheng, Shanbo and Wang, Mingxuan},
booktitle={ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
title={Unified Multimodal Punctuation Restoration Framework for Mixed-Modality Corpus},
year={2022},
volume={},
number={},
pages={7272-7276},
doi={10.1109/ICASSP43922.2022.9747131}}