# Two Approaches to Diachronic Normalization of Polish Texts

arXiv: https://arxiv.org/abs/2402.01300 · HTML: https://mlsanity.com/paper/2402.01300/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 26 references that are in the map.
- First posted on arXiv 2 Feb 2024.

## Builds on (most cited first, 7 of 7)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [RNN Approaches to Text Normalization: A Challenge](https://mlsanity.com/paper/1611.00068/) (2016)
- [A Large-Scale Comparison of Historical Text Normalization Systems](https://mlsanity.com/paper/1904.02036/) (2019)
- [Evaluation of Transfer Learning for Polish with a Text-to-Text Model](https://mlsanity.com/paper/2205.08808/) (2022)
- [Evaluating historical text normalization systems: How well do they generalize?](https://mlsanity.com/paper/1804.02545/) (2018)
- [Improving historical spelling normalization with bi-directional LSTMs and multi-task learning](https://mlsanity.com/paper/1610.07844/) (2016)

## Abstract (arXiv)

This paper discusses two approaches to the diachronic normalization of Polish texts: a rule-based solution that relies on a set of handcrafted patterns, and a neural normalization model based on the text-to-text transfer transformer architecture. The training and evaluation data prepared for the task are discussed in detail, along with experiments conducted to compare the proposed normalization solutions. A quantitative and qualitative analysis is made. It is shown that at the current stage of inquiry into the problem, the rule-based solution outperforms the neural one on 3 out of 4 variants of the prepared dataset, although in practice both approaches have distinct advantages and disadvantages.