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FullStop: Multilingual Deep Models for Punctuation Prediction

This work describes our contribution to the SEPP-NLG Shared Task in multilingual sentence segmentation and punctuation prediction. The goal of this task consists in training NLP models that can predict the end of sentence (EOS) and punctuation marks on automatically generated or transcribed texts. We show that these tasks benefit from crosslingual transfer by successfully employing multilingual deep language models. Our multilingual model achieves an average F1-score of 0.94 for EOS prediction on English, German, French, and Italian texts and an average F1-score of 0.78 for punctuation mark prediction.

Video Sample

How to use:

If you like to use the model on your own texts, you can use our python package.

Install

To get started install the package from pypi:

pip install deepmultilingualpunctuation

Make sure your system has cmake installed, it's a prerequisite.

Usage

The PunctuationModel class an process texts of any length. Note that processing of very long texts can be time consuming.

Restore Punctuation

from deepmultilingualpunctuation import PunctuationModel

model = PunctuationModel()
text = "My name is Clara and I live in Berkeley California Ist das eine Frage Frau Müller"
result = model.restore_punctuation(text)
print(result)

output

My name is Clara and I live in Berkeley, California. Ist das eine Frage, Frau Müller?

Predict Labels

from deepmultilingualpunctuation import PunctuationModel

model = PunctuationModel()
text = "My name is Clara and I live in Berkeley California Ist das eine Frage Frau Müller"
clean_text = model.preprocess(text)
labled_words = model.predict(clean_text)
print(labled_words)

output

[['My', '0', 0.9999887], ['name', '0', 0.99998665], ['is', '0', 0.9998579], ['Clara', '0', 0.6752215], ['and', '0', 0.99990904], ['I', '0', 0.9999877], ['live', '0', 0.9999839], ['in', '0', 0.9999515], ['Berkeley', ',', 0.99800044], ['California', '.', 0.99534047], ['Ist', '0', 0.99998784], ['das', '0', 0.99999154], ['eine', '0', 0.9999918], ['Frage', ',', 0.99622655], ['Frau', '0', 0.9999889], ['Müller', '?', 0.99863917]]

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:

Available Models

You can use different models by setting the model parameter:

model = PunctuationModel(model = "oliverguhr/fullstop-dutch-punctuation-prediction")

Training other languages

See training other languages

How to reproduce

In order to reproduce our experiments you can execute following code:

# setup
pip install -r requirements.txt
sh download-dataset.sh

# training
python model_test_suite.py 1
python model_test_suite.py 2

# test
sh predict_all_task_1_models.sh
sh predict_all_task_2_models.sh

Competition Website

https://sites.google.com/view/sentence-segmentation/

Data

https://drive.switch.ch/index.php/s/g3fMhMZU2uo32mf

Cite us

@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}
}