Revisiting Sample Size Determination in Natural Language Understanding
Ernie Chang Muhammad Hassan Rashid Pin-Jie Lin Changsheng Zhao Vera Demberg Yangyang Shi Vikas Chandra
Knowing exactly how many data points need to be labeled to achieve a certain
model performance is a hugely beneficial step towards reducing the overall
budgets for annotation. It pertains to both active learning and traditional
data annotation, and is particularly beneficial for low resource scenarios.
Nevertheless, it remains a largely under-explored area of research in NLP. We
therefore explored various techniques for estimating the training sample size
necessary to achieve a targeted performance value. We derived a simple yet
effective approach to predict the maximum achievable model performance based on
small amount of training samples - which serves as an early indicator during
data annotation for data quality and sample size determination. We performed
ablation studies on four language understanding tasks, and showed that the
proposed approach allows us to forecast model performance within a small margin
of mean absolute error (~ 0.9%) with only 10% data.