@inproceedings{pan-etal-2024-umuteam-semeval-2024,
title = "{UMUT}eam at {S}em{E}val-2024 Task 8: Combining Transformers and Syntax Features for Machine-Generated Text Detection",
author = "Pan, Ronghao and
Garc{\'\i}a-d{\'\i}az, Jos{\'e} Antonio and
Vivancos-vicente, Pedro Jos{\'e} and
Valencia-garc{\'\i}a, Rafael",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Tayyar Madabushi, Harish and
Da San Martino, Giovanni and
Rosenthal, Sara and
Ros{\'a}, Aiala},
booktitle = "Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.semeval-1.100",
doi = "10.18653/v1/2024.semeval-1.100",
pages = "697--702",
abstract = "These working notes describe the UMUTeam{'}s participation in Task 8 of SemEval-2024 entitled {``}Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated Text Detection{''}. This shared task aims at identifying machine-generated text in order to mitigate its potential misuse. This shared task is divided into three subtasks: Subtask A, a binary classification task to determine whether a given full-text was written by a human or generated by a machine; Subtask B, a multi-class classification problem to determine, given a full-text, who generated it. It can be written by a human or generated by a specific language model; and Subtask C, mixed human-machine text recognition. We participated in Subtask B, using an approach based on fine-tuning a pre-trained model, such as RoBERTa, combined with syntactic features of the texts. Our system placed 23rd out of a total of 77 participants, with a score of 75.350{\%}, outperforming the baseline.",
}
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<abstract>These working notes describe the UMUTeam’s participation in Task 8 of SemEval-2024 entitled “Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated Text Detection”. This shared task aims at identifying machine-generated text in order to mitigate its potential misuse. This shared task is divided into three subtasks: Subtask A, a binary classification task to determine whether a given full-text was written by a human or generated by a machine; Subtask B, a multi-class classification problem to determine, given a full-text, who generated it. It can be written by a human or generated by a specific language model; and Subtask C, mixed human-machine text recognition. We participated in Subtask B, using an approach based on fine-tuning a pre-trained model, such as RoBERTa, combined with syntactic features of the texts. Our system placed 23rd out of a total of 77 participants, with a score of 75.350%, outperforming the baseline.</abstract>
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%0 Conference Proceedings
%T UMUTeam at SemEval-2024 Task 8: Combining Transformers and Syntax Features for Machine-Generated Text Detection
%A Pan, Ronghao
%A García-díaz, José Antonio
%A Vivancos-vicente, Pedro José
%A Valencia-garcía, Rafael
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Tayyar Madabushi, Harish
%Y Da San Martino, Giovanni
%Y Rosenthal, Sara
%Y Rosá, Aiala
%S Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F pan-etal-2024-umuteam-semeval-2024
%X These working notes describe the UMUTeam’s participation in Task 8 of SemEval-2024 entitled “Multigenerator, Multidomain, and Multilingual Black-Box Machine-Generated Text Detection”. This shared task aims at identifying machine-generated text in order to mitigate its potential misuse. This shared task is divided into three subtasks: Subtask A, a binary classification task to determine whether a given full-text was written by a human or generated by a machine; Subtask B, a multi-class classification problem to determine, given a full-text, who generated it. It can be written by a human or generated by a specific language model; and Subtask C, mixed human-machine text recognition. We participated in Subtask B, using an approach based on fine-tuning a pre-trained model, such as RoBERTa, combined with syntactic features of the texts. Our system placed 23rd out of a total of 77 participants, with a score of 75.350%, outperforming the baseline.
%R 10.18653/v1/2024.semeval-1.100
%U https://aclanthology.org/2024.semeval-1.100
%U https://doi.org/10.18653/v1/2024.semeval-1.100
%P 697-702
Markdown (Informal)
[UMUTeam at SemEval-2024 Task 8: Combining Transformers and Syntax Features for Machine-Generated Text Detection](https://aclanthology.org/2024.semeval-1.100) (Pan et al., SemEval 2024)
ACL