  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.
