UM6P at SemEval-2023 Task 3: News genre classification based on transformers, graph convolution networks and number of sentences
Hamza Alami, Abdessamad Benlahbib, Abdelkader El Mahdaouy, Ismail Berrada
The 17th International Workshop on Semantic Evaluation (SemEval-2023) Task 3: detecting the category, the framing, and the persuasion techniques in online news in a multi-lingual setup Paper
TLDR:
This 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
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Abstract:
This 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.