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UM6P at SemEval-2023 Task 3: News genre classification based on transformers, graph convolution networks and number of sentences

Article scientifique 2023 Anglais

Résumé

This paper presents our proposed approach for English document genre classification in the context of SemEval-2023 Task 3, Subtask 1.Our method uses an ensemble technique to combine four distinct model predictions: Longformer, RoBERTa, GCN, and a sentence number-based model.Each model is optimized on simple and easy-to-understand objectives.We provide snippets of code that define each model to make the reading experience better.Our method ranked 12th in document genre (subtask 1) classification for English texts.

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Alami, H., Benlahbib, A., Mahdaouy, A., Berrada, I. (2023). UM6P at SemEval-2023 Task 3: News genre classification based on transformers, graph convolution networks and number of sentences. https://doi.org/10.18653/v1/2023.semeval-1.118

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Auteur(s)

Hamza Alami Abdessamad Benlahbib Abdelkader El Mahdaouy Ismaïl Berrada

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