Ciq@fire

Sinha, Manjira; Radhakrishnan, Priya; Mannarswamy, Sandya; Parida, Monnie

doi:10.1145/3368567.3368588

Cited by 2 publications

(2 citation statements)

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“…Improvement was noted when attention layers and part-of-speech vector representations were incorporated into the design process. The second approach, an ensemble of various machine learning, neural networks, and transformer-based models, provided the best overall performance. HateMonitors (Saha et al 2019) : To detect abusive content using pre-trained BERT and LASER sentence embeddings, the authors used zero-shot transfer learning and pre-trained sentence embeddings. For the system to be language-independent, they employed the Gradient Boosting model coupled with BERT and LASER embeddings. 3Idiots (Mishra and Mishra 2019) : BERT-based neural network models were used to refine the pre-trained monolingual and multi-lingual transformer models.…”

Section: Datasets and Experimental Settingmentioning

confidence: 99%

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A transformer-based multi-task framework for joint detection of aggression and hate on social media data

et al. 2023

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Moderators often face a double challenge regarding reducing offensive and harmful content in social media. Despite the need to prevent the free circulation of such content, strict censorship on social media cannot be implemented due to a tricky dilemma – preserving free speech on the Internet while limiting them and how not to overreact. Existing systems do not essentially exploit the correlatedness of hate-offensive content and aggressive posts; instead, they attend to the tasks individually. As a result, the need for cost-effective, sophisticated multi-task systems to effectively detect aggressive and offensive content on social media is highly critical in recent times. This work presents a novel multifaceted transformer-based framework to identify aggressive and hate posts on social media. Through an end-to-end transformer-based multi-task network, our proposed approach addresses the following array of tasks: (a) aggression identification, (b) misogynistic aggression identification, (c) identifying hate-offensive and non-hate-offensive content, (d) identifying hate, profane, and offensive posts, (e) type of offense. We further investigate the role of emotion in improving the system’s overall performance by learning the task of emotion detection jointly with the other tasks. We evaluate our approach on two popular benchmark datasets of aggression and hate speech, covering four languages, and compare the system performance with various state-of-the-art methods. Results indicate that our multi-task system performs significantly well for all the tasks across multiple languages, outperforming several benchmark methods. Moreover, the secondary task of emotion detection substantially improves the system performance for all the tasks, indicating strong correlatedness among the tasks of aggression, hate, and emotion, thus opening avenues for future research.

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Section: Datasets and Experimental Settingmentioning

confidence: 99%

“…HateMonitors (Saha et al 2019) : To detect abusive content using pre-trained BERT and LASER sentence embeddings, the authors used zero-shot transfer learning and pre-trained sentence embeddings. For the system to be language-independent, they employed the Gradient Boosting model coupled with BERT and LASER embeddings.…”

Section: Datasets and Experimental Settingmentioning

confidence: 99%