Abstract
This study investigates the detection of machine-generated text using several semantic embedding techniques, a critical issue in the era of advanced language models. Different methodologies were examined: GloVe embeddings, N-gram embedding models, Sentence BERT, and a concatenated embedding approach, against a fine-tuned RoBERTa baseline. The research was conducted within the framework of SemEval-2024 Task 8, encompassing tasks for binary and multi-class classification of machine-generated text.- Anthology ID:
- 2024.semeval-1.75
- Volume:
- Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Atul Kr. Ojha, A. Seza Doğruöz, Harish Tayyar Madabushi, Giovanni Da San Martino, Sara Rosenthal, Aiala Rosá
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 492–496
- Language:
- URL:
- https://aclanthology.org/2024.semeval-1.75
- DOI:
- Cite (ACL):
- Yuchen Wei. 2024. Team AT at SemEval-2024 Task 8: Machine-Generated Text Detection with Semantic Embeddings. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), pages 492–496, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- Team AT at SemEval-2024 Task 8: Machine-Generated Text Detection with Semantic Embeddings (Wei, SemEval 2024)
- PDF:
- https://preview.aclanthology.org/jeptaln-2024-ingestion/2024.semeval-1.75.pdf