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2023 SemEval SemEval 2023

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

Abstract

AbstractThis paper presents our proposed method for english documents genre classification in the context of SemEval 2023 task 3, subtask 1. Our method use ensemble technique to combine four distinct models predictions: Longformer, RoBERTa, GCN, and a sentences number-based model. Each model is optimized on simple objectives and easy to grasp. We provide snippets of code that define each model to make the reading experience better. Our method ranked 12th in documents genre classification for english texts.

Authors

Hamza Alami, Abdessamad Benlahbib, Abdelkader El Mahdaouy, Ismail Berrada

Topics

Machine Learning > Core Methods > Classification Deep Learning > Architectures > Transformers Deep Learning > Architectures > Graph Neural Networks

Keywords

transformer architecture ensemble learning graph convolution network genre classification

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