Pythia

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  • <a href="#Available">1 Available</a>
  • <a href="#Authors">2 Authors</a>
  • <a href="#Description">3 Description</a>
  • <a href="#References">4 References</a>
  • <a href="#Pythia_offline">5 Pythia offline</a>
  • <a href="#Media">65 Media</a>
  • <a href="#Contact">65 Contact</a>

Available

Authors

  • Thea Sommerschield, University of Oxford
  • Yannis Assael, DeepMind
  • Jonathan R. W. Prag, University of Oxford

Description

Ancient History relies on disciplines such as Epigraphy, the study of ancient inscribed texts, for evidence of the recorded past. However, these texts, "inscriptions", are often damaged over the centuries, and illegible parts of the text must be restored by specialists, known as epigraphists. This work presents a novel assistive method for providing text restorations using deep neural networks.

Pythia is the first ancient text restoration model that recovers missing characters from a damaged text input. Its architecture is carefully designed to handle long-term context information, and deal efficiently with missing or corrupted character and word representations.

To train it, we wrote a non-trivial pipeline to convert PHI, the largest digital corpus of ancient Greek inscriptions, to machine actionable text, which we call PHI-ML. On PHI-ML, Pythia's predictions achieve a 30.1% character error rate, compared to the 57.3% of human epigraphists. Moreover, in 73.5% of cases the ground-truth sequence was among the Top-20 hypotheses of Pythia, which effectively demonstrates the impact of such an assistive method on the field of digital epigraphy, and sets the state-of-the-art in ancient text restoration.

To aid further research in the field we created a freely accessible online interactive Python notebook, where researchers can query one of our models to get text restorations and visualise the attention weights.

Furthermore, the PHI-ML dataset and processing pipeline have been published on GitHub, so that any researcher may regenerate PHI-ML and train new models offline. Follow the instructions below under the section "Pythia offline".

References

Assael, Y., Sommerschield, T., Prag, J. “Restoring Ancient Text Using Deep Learning: A Case Study on Greek Epigraphy.” In Empirical Methods in Natural Language Processing (EMNLP - IJCNLP 2019). Association for Computational Linguistics. 6369-6376.

Available at:

When using any of this project's source code, please cite:

@inproceedings{assael2019restoring,
  title={Restoring ancient text using deep learning: a case study on {Greek} epigraphy},
  author={Assael, Yannis and Sommerschield, Thea and Prag, Jonathan},
  booktitle={Empirical Methods in Natural Language Processing},
  pages={6369--6376},
  year={2019}
}

Pythia offline

The following snippets provide references for regenerating PHI-ML and training new models offline.

Dependencies

pip install -r requirements.txt && \
python -m nltk.downloader punkt

PHI-ML dataset generation

# Download PHI (this will take a while)
python -c 'import pythia.data.phi_download; pythia.data.phi_download.main()'

# Process and generate PHI-ML
python -c 'import pythia.data.phi_process; pythia.data.phi_process.main()'

Preprocessed PHI-ML uploaded by @Holger.Danske800: link

Training

python -c 'import pythia.train; pythia.train.main()'

Evaluation

python -c 'import pythia.test; pythia.test.main()' --load_checkpoint="your_model_path/"

Docker execution

./build.sh
./run.sh <GPU_ID> python -c 'import pythia.train; pythia.train.main()'

Media

Our research has been featured in blog posts by DeepMind and the University of Oxford, and has been published in articles on The Times, NewScientist, La Repubblica, Ekathimerini, TechCrunch and others.

It has also been presented at EMNLP - IJCNLP 2019 in Hong Kong, at the British School at Rome, the Oxford Epigraphy Workshop, the OIKOS National Research School in Classical Studies, during lectures at Venice Ca' Foscari and Rome La Sapienza, and most recently at the Digital Classicist London Seminar.

Pythia is a partner project of epigraphy.info.

Contact