Teodor-George Marchitan

Also published as: Teodor-george Marchitan


2024

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Verba volant, scripta volant? Don’t worry! There are computational solutions for protoword reconstruction
Liviu Dinu | Ana Uban | Alina Cristea | Ioan-Bogdan Iordache | Teodor-George Marchitan | Simona Georgescu | Laurentiu Zoicas
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing

We introduce a new database of cognate words and etymons for the five main Romance languages, the most comprehensive one to date. We propose a strong benchmark for the automatic reconstruction of protowords for Romance languages, by applying a set of machine learning models and features on these data. The best results reach 90% accuracy in predicting the protoword of a given cognate set, surpassing existing state-of-the-art results for this task and showing that computational methods can be very useful in assisting linguists with protoword reconstruction.

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Team Unibuc - NLP at SemEval-2024 Task 8: Transformer and Hybrid Deep Learning Based Models for Machine-Generated Text Detection
Teodor-george Marchitan | Claudiu Creanga | Liviu P. Dinu
Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)

This paper describes the approach of the UniBuc - NLP team in tackling the SemEval 2024 Task 8: Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated Text Detection. We explored transformer-based and hybrid deep learning architectures. For subtask B, our transformer-based model achieved a strong second-place out of 77 teams with an accuracy of 86.95%, demonstrating the architecture’s suitability for this task. However, our models showed overfitting in subtask A which could potentially be fixed with less fine-tunning and increasing maximum sequence length. For subtask C (token-level classification), our hybrid model overfit during training, hindering its ability to detect transitions between human and machine-generated text.