mlsanityData to 7 Oct 2026

A Federated Approach to Predicting Emojis in Hindi Tweets

arXiv 2211.06401 ↗First posted 11 Nov 2022cs.LG cs.CLcited by 5

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Abstract (arXiv)

The use of emojis affords a visual modality to, often private, textual communication. The task of predicting emojis however provides a challenge for machine learning as emoji use tends to cluster into the frequently used and the rarely used emojis. Much of the machine learning research on emoji use has focused on high resource languages and has conceptualised the task of predicting emojis around traditional server-side machine learning approaches. However, traditional machine learning approaches for private communication can introduce privacy concerns, as these approaches require all data to be transmitted to a central storage. In this paper, we seek to address the dual concerns of emphasising high resource languages for emoji prediction and risking the privacy of people's data. We introduce a new dataset of 118k tweets (augmented from 25k unique tweets) for emoji prediction in Hindi, and propose a modification to the federated learning algorithm, CausalFedGSD, which aims to strike a balance between model performance and user privacy. We show that our approach obtains comparative scores with more complex centralised models while reducing the amount of data required to optimise the models and minimising risks to user privacy.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 16 of its 50 references that are in the map. First posted on arXiv 11 Nov 2022.

Builds on

16 of 50 references.

References · 16

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4 more
  1. 2018Deep Imbalanced Attribute Classification using Visual Attention Aggregationcited by 56
  2. 2021Towards Causal Federated Learning For Enhanced Robustness and Privacycited by 14
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  4. 2018Contrastive Learning of Emoji-based Representations for Resource-Poor Languagescited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 1; 2025: 0; 2026: 1.

5 papers.

  1. 2023Communication Efficient Federated Learning for Multilingual Neural Machine Translation with Adaptercited by 8
  2. 2023Federated Learning Based Multilingual Emoji Prediction In Clean and Attack Scenarioscited by 3
  3. 2024On-Device Emoji Classifier Trained with GPT-based Data Augmentation for a Mobile Keyboardcited by 2
  4. 2023A Federated Approach for Hate Speech Detectioncited by 1
  5. 2026FedMental: Evaluating Federated Learning for Mental Health Detection from Social Media Datacited by 0