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2022 ACL ACL 2022

giniUs @LT-EDI-ACL2022: Aasha: Transformers based Hope-EDI

Abstract

AbstractThis paper describes team giniUs’ submission to the Hope Speech Detection for Equality, Diversity and Inclusion Shared Task organised by LT-EDI ACL 2022. We have fine-tuned the Roberta-large pre-trained model and extracted the last four decoder layers to build a classifier. Our best result on the leaderboard achieve a weighted F1 score of 0.86 and a Macro F1 score of 0.51 for English. We have secured a rank of 4 for the English task. We have open-sourced our code implementations on GitHub to facilitate easy reproducibility by the scientific community.

🌉 Interdisciplinary Bridge - Artificial Intelligence and Healthcare & Medicine and Interdisciplinary and Natural Language Processing

Authors

Basavraj Chinagundi, Harshul Surana

Topics

Machine Learning > Core Methods > Classification Deep Learning > Architectures > Transformers Natural Language Processing > Applications > Text Classification Healthcare & Medicine > Clinical > Mental Health Interdisciplinary > Linguistics > Computational Linguistics Interdisciplinary > Social > Social Media Analysis Deep Learning > Learning Types > Fine-Tuning

Keywords

text classification hope speech detection social media text roberta model transformer model roberta fine-tuning equality diversity inclusion weighted f1 score

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