Relation-based Counterfactual Data Augmentation and Contrastive Learning for Robustifying Natural Language Inference Models
This addresses robustness issues in natural language inference models for NLP applications, but it is incremental as it builds on existing augmentation and contrastive learning techniques.
The paper tackles the problem of natural language inference models relying on non-causal features by proposing a method using counterfactual data augmentation and contrastive learning, which improves performance and robustness on counterfactually-revised and general NLI datasets.
Although pre-trained language models show good performance on various natural language processing tasks, they often rely on non-causal features and patterns to determine the outcome. For natural language inference tasks, previous results have shown that even a model trained on a large number of data fails to perform well on counterfactually revised data, indicating that the model is not robustly learning the semantics of the classes. In this paper, we propose a method in which we use token-based and sentence-based augmentation methods to generate counterfactual sentence pairs that belong to each class, and apply contrastive learning to help the model learn the difference between sentence pairs of different classes with similar contexts. Evaluation results with counterfactually-revised dataset and general NLI datasets show that the proposed method can improve the performance and robustness of the NLI model.