@inproceedings{alami-etal-2023-um6p,
    title = "{UM}6{P} at {S}em{E}val-2023 Task 3: News genre classification based on transformers, graph convolution networks and number of sentences",
    author = "Alami, Hamza  and
      Benlahbib, Abdessamad  and
      El Mahdaouy, Abdelkader  and
      Berrada, Ismail",
    editor = {Ojha, Atul Kr.  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Da San Martino, Giovanni  and
      Tayyar Madabushi, Harish  and
      Kumar, Ritesh  and
      Sartori, Elisa},
    booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.semeval-1.118",
    doi = "10.18653/v1/2023.semeval-1.118",
    pages = "856--861",
    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.",
}

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.
Alami, Hamza Benlahbib, Abdessamad El Mahdaouy, Abdelkader Berrada, Ismail
UM6P at SemEval-2023 Task 3: News genre classification based on transformers, graph convolution networks and number of sentences