@inproceedings{maity-etal-2023-genex,
title = "{G}en{E}x: A Commonsense-aware Unified Generative Framework for Explainable Cyberbullying Detection",
author = "Maity, Krishanu and
Jain, Raghav and
Jha, Prince and
Saha, Sriparna and
Bhattacharyya, Pushpak",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.1035",
doi = "10.18653/v1/2023.emnlp-main.1035",
pages = "16632--16645",
abstract = "With the rise of social media and online communication, the issue of cyberbullying has gained significant prominence. While extensive research is being conducted to develop more effective models for detecting cyberbullying in monolingual languages, a significant gap exists in understanding code-mixed languages and the need for explainability in this context. To address this gap, we have introduced a novel benchmark dataset named BullyExplain for explainable cyberbullying detection in code-mixed language. In this dataset, each post is meticulously annotated with four labels: bully, sentiment, target, and rationales, indicating the specific phrases responsible for identifying the post as a bully. Our current research presents an innovative unified generative framework, GenEx, which reimagines the multitask problem as a text-to-text generation task. Our proposed approach demonstrates its superiority across various evaluation metrics when applied to the BullyExplain dataset, surpassing other baseline models and current state-of-the-art approaches.",
}
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<abstract>With the rise of social media and online communication, the issue of cyberbullying has gained significant prominence. While extensive research is being conducted to develop more effective models for detecting cyberbullying in monolingual languages, a significant gap exists in understanding code-mixed languages and the need for explainability in this context. To address this gap, we have introduced a novel benchmark dataset named BullyExplain for explainable cyberbullying detection in code-mixed language. In this dataset, each post is meticulously annotated with four labels: bully, sentiment, target, and rationales, indicating the specific phrases responsible for identifying the post as a bully. Our current research presents an innovative unified generative framework, GenEx, which reimagines the multitask problem as a text-to-text generation task. Our proposed approach demonstrates its superiority across various evaluation metrics when applied to the BullyExplain dataset, surpassing other baseline models and current state-of-the-art approaches.</abstract>
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%0 Conference Proceedings
%T GenEx: A Commonsense-aware Unified Generative Framework for Explainable Cyberbullying Detection
%A Maity, Krishanu
%A Jain, Raghav
%A Jha, Prince
%A Saha, Sriparna
%A Bhattacharyya, Pushpak
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F maity-etal-2023-genex
%X With the rise of social media and online communication, the issue of cyberbullying has gained significant prominence. While extensive research is being conducted to develop more effective models for detecting cyberbullying in monolingual languages, a significant gap exists in understanding code-mixed languages and the need for explainability in this context. To address this gap, we have introduced a novel benchmark dataset named BullyExplain for explainable cyberbullying detection in code-mixed language. In this dataset, each post is meticulously annotated with four labels: bully, sentiment, target, and rationales, indicating the specific phrases responsible for identifying the post as a bully. Our current research presents an innovative unified generative framework, GenEx, which reimagines the multitask problem as a text-to-text generation task. Our proposed approach demonstrates its superiority across various evaluation metrics when applied to the BullyExplain dataset, surpassing other baseline models and current state-of-the-art approaches.
%R 10.18653/v1/2023.emnlp-main.1035
%U https://aclanthology.org/2023.emnlp-main.1035
%U https://doi.org/10.18653/v1/2023.emnlp-main.1035
%P 16632-16645
Markdown (Informal)
[GenEx: A Commonsense-aware Unified Generative Framework for Explainable Cyberbullying Detection](https://aclanthology.org/2023.emnlp-main.1035) (Maity et al., EMNLP 2023)
ACL