# Curriculum Learning Strategies for Hindi-English Codemixed Sentiment Analysis

## TL;DR

This paper introduces curriculum learning strategies tailored for Hindi-English code-mixed sentiment analysis, significantly improving accuracy and robustness over existing methods in resource-scarce, noisy social media text data.

## Contribution

It proposes novel curriculum learning approaches specifically designed for code-mixed NLP tasks, addressing data scarcity and language divergence challenges.

## Key findings

- Outperforms state-of-the-art by 3.31% accuracy
- Enhances model robustness and convergence stability
- Effective for resource-scarce, noisy social media data

## Abstract

Sentiment Analysis and other semantic tasks are commonly used for social media textual analysis to gauge public opinion and make sense from the noise on social media. The language used on social media not only commonly diverges from the formal language, but is compounded by codemixing between languages, especially in large multilingual societies like India. Traditional methods for learning semantic NLP tasks have long relied on end to end task specific training, requiring expensive data creation process, even more so for deep learning methods. This challenge is even more severe for resource scarce texts like codemixed language pairs, with lack of well learnt representations as model priors, and task specific datasets can be few and small in quantities to efficiently exploit recent deep learning approaches. To address above challenges, we introduce curriculum learning strategies for semantic tasks in code-mixed Hindi-English (Hi-En) texts, and investigate various training strategies for enhancing model performance. Our method outperforms the state of the art methods for Hi-En codemixed sentiment analysis by 3.31% accuracy, and also shows better model robustness in terms of convergence, and variance in test performance.