Adaptive Ranking-based Sample Selection for Weakly Supervised Class-imbalanced Text Classification
This work addresses data imbalance in weakly supervised NLP tasks, offering a practical solution for scenarios with imbalanced and cheaply labeled data, though it is incremental as it builds on existing weak supervision methods.
The paper tackles the overlooked issue of data imbalance in weakly supervised text classification by proposing ARS2, a model-agnostic framework that uses adaptive ranking-based sample selection to balance data batches and leverage labeling rules, resulting in a 2%-57.8% improvement in F1-score across four datasets with varying imbalance ratios.
To obtain a large amount of training labels inexpensively, researchers have recently adopted the weak supervision (WS) paradigm, which leverages labeling rules to synthesize training labels rather than using individual annotations to achieve competitive results for natural language processing (NLP) tasks. However, data imbalance is often overlooked in applying the WS paradigm, despite being a common issue in a variety of NLP tasks. To address this challenge, we propose Adaptive Ranking-based Sample Selection (ARS2), a model-agnostic framework to alleviate the data imbalance issue in the WS paradigm. Specifically, it calculates a probabilistic margin score based on the output of the current model to measure and rank the cleanliness of each data point. Then, the ranked data are sampled based on both class-wise and rule-aware ranking. In particular, the two sample strategies corresponds to our motivations: (1) to train the model with balanced data batches to reduce the data imbalance issue and (2) to exploit the expertise of each labeling rule for collecting clean samples. Experiments on four text classification datasets with four different imbalance ratios show that ARS2 outperformed the state-of-the-art imbalanced learning and WS methods, leading to a 2%-57.8% improvement on their F1-score.