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
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.- Anthology ID:
- 2023.semeval-1.118
- Volume:
- Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Atul Kr. Ojha, A. Seza Doğruöz, Giovanni Da San Martino, Harish Tayyar Madabushi, Ritesh Kumar, Elisa Sartori
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 856–861
- Language:
- URL:
- https://aclanthology.org/2023.semeval-1.118
- DOI:
- 10.18653/v1/2023.semeval-1.118
- Cite (ACL):
- Hamza Alami, Abdessamad Benlahbib, Abdelkader El Mahdaouy, and Ismail Berrada. 2023. UM6P at SemEval-2023 Task 3: News genre classification based on transformers, graph convolution networks and number of sentences. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 856–861, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- UM6P at SemEval-2023 Task 3: News genre classification based on transformers, graph convolution networks and number of sentences (Alami et al., SemEval 2023)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2023.semeval-1.118.pdf