Generating Data to Mitigate Spurious Correlations in Natural Language Inference Datasets
This addresses the issue of poor generalization in NLP models for researchers and practitioners, though it is incremental as it builds on existing debiasing strategies.
The paper tackles the problem of spurious correlations in natural language inference datasets by generating debiased versions of SNLI and MNLI, resulting in models that generalize better across various test sets, with improvements such as outperforming previous state-of-the-art on SNLI-hard and MNLI-hard when combined with product-of-experts.
Natural language processing models often exploit spurious correlations between task-independent features and labels in datasets to perform well only within the distributions they are trained on, while not generalising to different task distributions. We propose to tackle this problem by generating a debiased version of a dataset, which can then be used to train a debiased, off-the-shelf model, by simply replacing its training data. Our approach consists of 1) a method for training data generators to generate high-quality, label-consistent data samples; and 2) a filtering mechanism for removing data points that contribute to spurious correlations, measured in terms of z-statistics. We generate debiased versions of the SNLI and MNLI datasets, and we evaluate on a large suite of debiased, out-of-distribution, and adversarial test sets. Results show that models trained on our debiased datasets generalise better than those trained on the original datasets in all settings. On the majority of the datasets, our method outperforms or performs comparably to previous state-of-the-art debiasing strategies, and when combined with an orthogonal technique, product-of-experts, it improves further and outperforms previous best results of SNLI-hard and MNLI-hard.