metadata
language:
- en
- de
- fr
- it
tags:
- punctuation prediction
- punctuation
datasets: wmt/europarl
license: mit
widget:
- text: Ho sentito che ti sei laureata il che mi fa molto piacere
example_title: Italian
- text: Tous les matins vers quatre heures mon père ouvrait la porte de ma chambre
example_title: French
- text: Ist das eine Frage Frau Müller
example_title: German
- text: My name is Clara and I live in Berkeley California
example_title: English
metrics:
- f1
This model predicts the punctuation of English, Italian, French and German texts. We developed it to restore the punctuation of transcribed spoken language.
This multilanguage model was trained on the Europarl Dataset provided by the SEPP-NLG Shared Task. Please note that this dataset consists of political speeches. Therefore the model might perform differently on texts from other domains.
The model restores the following punctuation markers: "." "," "?" "-" ":"
Results
The performance differs for the single punctuation markers as hyphens and colons, in many cases, are optional and can be substituted by either a comma or a full stop. The model achieves the following F1 scores for the different languages:
| Label | EN | DE | FR | IT |
|---|---|---|---|---|
| 0 | 0.991 | 0.997 | 0.992 | 0.989 |
| . | 0.948 | 0.961 | 0.945 | 0.942 |
| ? | 0.890 | 0.893 | 0.871 | 0.832 |
| , | 0.819 | 0.945 | 0.831 | 0.798 |
| : | 0.575 | 0.652 | 0.620 | 0.588 |
| - | 0.425 | 0.435 | 0.431 | 0.421 |
| macro average | 0.775 | 0.814 | 0.782 | 0.762 |
References
@article{guhr-EtAl:2021:fullstop,
title={FullStop: Multilingual Deep Models for Punctuation Prediction},
author = {Guhr, Oliver and Schumann, Anne-Kathrin and Bahrmann, Frank and Böhme, Hans Joachim},
booktitle = {Proceedings of the Swiss Text Analytics Conference 2021},
month = {June},
year = {2021},
address = {Winterthur, Switzerland},
publisher = {CEUR Workshop Proceedings},
url = {http://ceur-ws.org/Vol-2957/sepp_paper4.pdf}
}