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Automatic punctuation restoration with BERT models

arXiv 2101.07343 ↗First posted 18 Jan 2021cs.CLcited by 12

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Abstract (arXiv)

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.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 6 of its 23 references that are in the map. First posted on arXiv 18 Jan 2021.

Builds on

6 of 23 references.

References · 6

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2019ALBERT: A Lite BERT for Self-supervised Learning of Language Representationscited by 2,772
  4. 2020Controllable Time-Delay Transformer for Real-Time Punctuation Prediction and Disfluency Detectioncited by 23
  5. 2018Punctuation Prediction Model for Conversational Speechcited by 20
  6. 2019Fast and Accurate Capitalization and Punctuation for Automatic Speech Recognition Using Transformer and Chunk Mergingcited by 14

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 3; 2023: 3; 2024: 1; 2025: 1; 2026: 1.

12 papers.