Punctuation Prediction for Polish Texts using Transformers
Fuente:
arXiv
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866910636705841152 |
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| author | Pokrywka, Jakub |
| author_facet | Pokrywka, Jakub |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_04621 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Punctuation Prediction for Polish Texts using Transformers Pokrywka, Jakub Computation and Language 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. |
| title | Punctuation Prediction for Polish Texts using Transformers |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2410.04621 |