While Curriculum learning (CL) has recently gained traction in Natural language Processing Tasks, it still isn't being analyzed adequately. Previous works only show their effectiveness but fail short to fully explain and interpret the internal workings. In this paper, we analyze curriculum learning in sentiment analysis along multiple axes. Some of these axes have been proposed by earlier works that need deeper study. Such analysis requires understanding where curriculum learning works and where it doesn't. Our axes of analysis include Task difficulty on CL, comparing CL pacing techniques, and qualitative analysis by visualizing the movement of attention scores in the model as curriculum phases progress. We find that curriculum learning works best for difficult tasks and may even lead to a decrement in performance for tasks that have higher performance without curriculum learning. Within curriculum pacing, we see that One-Pass curriculum strategies suffer from catastrophic forgetting and attention movement visualization shows that curriculum learning breaks down the main task into easier sub-tasks which the model solves easily.

本文分析了课程学习在情感分析中的应用，研究了其对任务难度、课程学习技术和注意力分数的影响， 结果表明课程学习在困难任务上效果最好，但在表现较好的任务上可能导致性能下降，并发现一次性课程策略容易出现灾难性遗忘。

分析基于课程学习的情感分析在任务难度、节奏和可视化方面的表现