Revisiting Sample Size Determination in Natural Language Understanding
This addresses the challenge of reducing annotation budgets for NLP practitioners, particularly in low-resource scenarios, though it is incremental as it builds on existing sample size estimation techniques.
The paper tackled the problem of determining how many labeled data points are needed to achieve a target model performance in natural language understanding, and showed that their approach can forecast performance with a mean absolute error of about 0.9% using only 10% of the data.
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.