Model Details

simplify_dutch

This is the source code for my thesis on "Controllable Sentence Simplification in Dutch" in the Masters of AI at KU Leuven. The full code can be found at: https://github.com/tsei902/simplify_dutch

Data

The origin of the datasets in resources/datasets is:

Wikilarge, available under: https://github.com/XingxingZhang/dress The wikilarge data is limited the first 10000 rows.
ASSET, available under: https://github.com/facebookresearch Which both have been translated to Dutch.

Model

The Dutch T5 model t5-base-dutch from Hugging Face has been adopted and trained on the task of sentence simplification. The folder /saved model contains the final trained model on 10000 rows of data, as stated in the Thesis.

Sequence:

TRAINING DATA needs preprocessing with preprocessor.py
Generation can be done with generate_on_pretrained.py with a prior adjustment of
The generation parameters in model.simplify() where the decoding method needs to be chosen (Greedy decoding, Top-p & top-k, or Beam search)
Manual scoring of a generated text is possible with evaluate.py

Further remarks:

The folder resources/processed data contains the training set with the prepended control tokens
The folder resources/DUMPS contains the Word embeddings from Fares et al. (2017) have been used. The data is available under: http://vectors.nlpl.eu/repository. (Fares, M., Kutuzov, A., Oepen, S., & Velldal, E. (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources. Proceedings of the 21st Nordic Conference on Computational Linguistics, Gothenburg, Sweden.)
The folder resources/outputs/final_decoder_outputs contains the final generated text per decoding strategy (Greedy decoding, Top-p & top-k, or Beam search) for both the full test set and the sample dataset
The folder translations contains sampled text (106 and 84 rows) from the original English datasets (WIKILarge and ASSET), a machine-translated version as well as the human translated references.

Credits

The preprocessor.py and the utils.py contain code that has been adapted from https://github.com/KimChengSHEANG/TS_T5 (Sheang, K. C., & Saggion, H. (2021). Controllable Sentence Simplification with a Unified Text-to-Text Transfer Transformer.INLG 2021 International Conference on Natural Language Generation, Aberdeen, Scotland, UK.) The preprocessor.py has been adapted to the usage of Dutch.

Model Description

Developed by: Theresa Seidl
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Language(s) (NLP): Dutsch
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Finetuned from model [optional]: https://huggingface.co/yhavinga/t5-base-dutch

Model Sources [optional]

Repository: https://github.com/tsei902/simplify_dutch
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Uses

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How to Get Started with the Model

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Training Details

Training Data

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Training Procedure

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Evaluation

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Summary

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