Contrastive Learning of Emoji-based Representations for Resource-Poor Languages
This addresses the challenge of limited linguistic resources for languages like Hindi and Telugu in social media applications, though it is incremental as it builds on existing contrastive learning and siamese network techniques.
The paper tackles the problem of learning emoji-based representations for resource-poor languages by proposing CESNA, a siamese network with contrastive loss, which outperforms state-of-the-art emoji prediction methods on Twitter datasets including English, Spanish, Hindi, and Telugu.
The introduction of emojis (or emoticons) in social media platforms has given the users an increased potential for expression. We propose a novel method called Classification of Emojis using Siamese Network Architecture (CESNA) to learn emoji-based representations of resource-poor languages by jointly training them with resource-rich languages using a siamese network. CESNA model consists of twin Bi-directional Long Short-Term Memory Recurrent Neural Networks (Bi-LSTM RNN) with shared parameters joined by a contrastive loss function based on a similarity metric. The model learns the representations of resource-poor and resource-rich language in a common emoji space by using a similarity metric based on the emojis present in sentences from both languages. The model, hence, projects sentences with similar emojis closer to each other and the sentences with different emojis farther from one another. Experiments on large-scale Twitter datasets of resource-rich languages - English and Spanish and resource-poor languages - Hindi and Telugu reveal that CESNA outperforms the state-of-the-art emoji prediction approaches based on distributional semantics, semantic rules, lexicon lists and deep neural network representations without shared parameters.