Papers › Automatic punctuation restoration with BERT models

Automatic punctuation restoration with BERT models

18 Jan 2021arXiv:2101.07343archive 2025-07-28

Attila Nagy, Bence Bial, Judit Ács

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.

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attilanagy234/neural-punctuator officialmentioned in papermentioned on GitHubpytorch report

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Punctuation Restoration

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Adam Attention Attention Dropout BERT Dense Connections Dropout Layer Normalization Linear Layer Linear Warmup With Linear Decay Multi-Head Attention Residual Connection Softmax Weight Decay WordPiece

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