# Punctuation Prediction for Polish Texts using Transformers

arXiv: https://arxiv.org/abs/2410.04621 · HTML: https://mlsanity.com/paper/2410.04621/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 6 Oct 2024.

## Builds on (most cited first, 5 of 5)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [HerBERT: Efficiently Pretrained Transformer-based Language Model for Polish](https://mlsanity.com/paper/2105.01735/) (2021)
- [Pre-training Polish Transformer-based Language Models at Scale](https://mlsanity.com/paper/2006.04229/) (2020)
- [Robust Prediction of Punctuation and Truecasing for Medical ASR](https://mlsanity.com/paper/2007.02025/) (2020)

## Abstract (arXiv)

Speech recognition systems typically output text lacking punctuation. However, punctuation is crucial for written text comprehension. To tackle this problem, Punctuation Prediction models are developed. This paper describes a solution for Poleval 2022 Task 1: Punctuation Prediction for Polish Texts, which scores 71.44 Weighted F1. The method utilizes a single HerBERT model finetuned to the competition data and an external dataset.