Papers by Jash Mehta

2 papers
A Federated Approach to Predicting Emojis in Hindi Tweets (2022.emnlp-main)

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Challenge: emojis are a visual modality to, often private, textual communication, but their use tends to cluster into the frequently used and the rarely used eojis.
Approach: They propose to use 118k tweets to predict emojis in Hindi and a federated learning algorithm to achieve a balance between model performance and user privacy.
Outcome: The proposed approach achieves comparative scores with more complex centralised models while minimising risks to user privacy.
A Federated Approach for Hate Speech Detection (2023.eacl-main)

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Challenge: Despite the scale of social media content, privacy preservation in hate speech detection has remained understudied.
Approach: They propose to use federated machine learning to address privacy concerns in hate speech detection by obtaining a 6.81% improvement in F1-score.
Outcome: The proposed method improves the F1-score of hate speech detection by 6.81% while maintaining public data privacy.

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