TY - JOUR
T1 - Regret and Hope on Transformers
T2 - An Analysis of Transformers on Regret and Hope Speech Detection Datasets
AU - Sidorov, Grigori
AU - Balouchzahi, Fazlourrahman
AU - Butt, Sabur
AU - Gelbukh, Alexander
N1 - Publisher Copyright:
© 2023 by the authors.
PY - 2023/3
Y1 - 2023/3
N2 - In this paper, we analyzed the performance of different transformer models for regret and hope speech detection on two novel datasets. For the regret detection task, we compared the averaged macro-scores of the transformer models to the previous state-of-the-art results. We found that the transformer models outperformed the previous approaches. Specifically, the roberta-based model achieved the highest averaged macro F1-score of 0.83, beating the previous state-of-the-art score of 0.76. For the hope speech detection task, the bert-based, uncased model achieved the highest averaged-macro F1-score of 0.72 among the transformer models. However, the specific performance of each model varied slightly depending on the task and dataset. Our findings highlight the effectiveness of transformer models for hope speech and regret detection tasks, and the importance of considering the effects of context, specific transformer architectures, and pre-training on their performance.
AB - In this paper, we analyzed the performance of different transformer models for regret and hope speech detection on two novel datasets. For the regret detection task, we compared the averaged macro-scores of the transformer models to the previous state-of-the-art results. We found that the transformer models outperformed the previous approaches. Specifically, the roberta-based model achieved the highest averaged macro F1-score of 0.83, beating the previous state-of-the-art score of 0.76. For the hope speech detection task, the bert-based, uncased model achieved the highest averaged-macro F1-score of 0.72 among the transformer models. However, the specific performance of each model varied slightly depending on the task and dataset. Our findings highlight the effectiveness of transformer models for hope speech and regret detection tasks, and the importance of considering the effects of context, specific transformer architectures, and pre-training on their performance.
KW - contextual embedding
KW - hope speech detection
KW - regret detection
KW - text classification
KW - transformers
UR - http://www.scopus.com/inward/record.url?scp=85151968365&partnerID=8YFLogxK
U2 - 10.3390/app13063983
DO - 10.3390/app13063983
M3 - Artículo
AN - SCOPUS:85151968365
SN - 2076-3417
VL - 13
JO - Applied Sciences (Switzerland)
JF - Applied Sciences (Switzerland)
IS - 6
M1 - 3983
ER -