C2L: Causally Contrastive Learning for Robust Text Classification
Keywords:Speech & Natural Language Processing (SNLP)
AbstractDespite the super-human accuracy of recent deep models in NLP tasks, their robustness is reportedly limited due to their reliance on spurious patterns. We thus aim to leverage contrastive learning and counterfactual augmentation for robustness. For augmentation, existing work either requires humans to add counterfactuals to the dataset or machines to automatically matches near-counterfactuals already in the dataset. Unlike existing augmentation is affected by spurious correlations, ours, by synthesizing “a set” of counterfactuals, and making a collective decision on the distribution of predictions on this set, can robustly supervise the causality of each term. Our empirical results show that our approach, by collective decisions, is less sensitive to task model bias of attribution-based synthesis, and thus achieves significant improvements, in diverse dimensions: 1) counterfactual robustness, 2) cross-domain generalization, and 3) generalization from scarce data.
How to Cite
Choi, S., Jeong, M., Han, H., & Hwang, S.- won. (2022). C2L: Causally Contrastive Learning for Robust Text Classification. Proceedings of the AAAI Conference on Artificial Intelligence, 36(10), 10526-10534. https://doi.org/10.1609/aaai.v36i10.21296
AAAI Technical Track on Speech and Natural Language Processing