Learning Emotion from 100 Observations: Unexpected Robustness of Deep Learning under Strong Data Limitations

Understand

One of the major downsides of Deep Learning is its supposed need for vast amounts of training data.

• As such, these techniques appear ill-suited for NLP areas where annotated data is limited, such as less-resourced languages or emotion analysis, with its many nuanced and hard-to-acquire annotation formats.
• We conduct a questionnaire study indicating that indeed the vast majority of researchers in emotion analysis deems neural models inferior to traditional machine learning when training data is limited.
• In stark contrast to those survey results, we provide empirical evidence for English, Polish, and Portuguese that commonly used neural architectures can be trained on surprisingly few observations, outperforming $n$-gram based ridge regression on only 100 data points.