We present an approach for automatic punctuation restoration with BERT models for English and Hungarian. For English, we conduct our experiments on Ted Talks, a commonly used benchmark for punctuation restoration, while for Hungarian we evaluate our models on the Szeged Treebank dataset. Our best models achieve a macro-averaged F1-score of 79.8 in English and 82.2 in Hungarian. Our code is publicly available.
@article{arxiv.2101.07343,
title = {Automatic punctuation restoration with BERT models},
author = {Attila Nagy and Bence Bial and Judit Ács},
journal= {arXiv preprint arXiv:2101.07343},
year = {2021}
}
Comments
11 pages, 6 figures, source code at https://github.com/attilanagy234/neural-punctuator