Émilie Pagé-Perron


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How Low is Too Low? A Computational Perspective on Extremely Low-Resource Languages
Rachit Bansal | Himanshu Choudhary | Ravneet Punia | Niko Schenk | Émilie Pagé-Perron | Jacob Dahl
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: Student Research Workshop

Despite the recent advancements of attention-based deep learning architectures across a majority of Natural Language Processing tasks, their application remains limited in a low-resource setting because of a lack of pre-trained models for such languages. In this study, we make the first attempt to investigate the challenges of adapting these techniques to an extremely low-resource language – Sumerian cuneiform – one of the world’s oldest written languages attested from at least the beginning of the 3rd millennium BC. Specifically, we introduce the first cross-lingual information extraction pipeline for Sumerian, which includes part-of-speech tagging, named entity recognition, and machine translation. We introduce InterpretLR, an interpretability toolkit for low-resource NLP and use it alongside human evaluations to gauge the trained models. Notably, all our techniques and most components of our pipeline can be generalised to any low-resource language. We publicly release all our implementations including a novel data set with domain-specific pre-processing to promote further research in this domain.


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Towards the First Machine Translation System for Sumerian Transliterations
Ravneet Punia | Niko Schenk | Christian Chiarcos | Émilie Pagé-Perron
Proceedings of the 28th International Conference on Computational Linguistics

The Sumerian cuneiform script was invented more than 5,000 years ago and represents one of the oldest in history. We present the first attempt to translate Sumerian texts into English automatically. We publicly release high-quality corpora for standardized training and evaluation and report results on experiments with supervised, phrase-based, and transfer learning techniques for machine translation. Quantitative and qualitative evaluations indicate the usefulness of the translations. Our proposed methodology provides a broader audience of researchers with novel access to the data, accelerates the costly and time-consuming manual translation process, and helps them better explore the relationships between Sumerian cuneiform and Mesopotamian culture.


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Towards a Linked Open Data Edition of Sumerian Corpora
Christian Chiarcos | Émilie Pagé-Perron | Ilya Khait | Niko Schenk | Lucas Reckling
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)


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Machine Translation and Automated Analysis of the Sumerian Language
Émilie Pagé-Perron | Maria Sukhareva | Ilya Khait | Christian Chiarcos
Proceedings of the Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature

This paper presents a newly funded international project for machine translation and automated analysis of ancient cuneiform languages where NLP specialists and Assyriologists collaborate to create an information retrieval system for Sumerian. This research is conceived in response to the need to translate large numbers of administrative texts that are only available in transcription, in order to make them accessible to a wider audience. The methodology includes creation of a specialized NLP pipeline and also the use of linguistic linked open data to increase access to the results.