Conditional Natural Language Inference

To properly explain sentence pairs that provide contradictory (different) information for different conditions, we introduce the task of conditional natural language inference (Cond-NLI) and focus on automatically extracting contradictory aspects and their conditions from a sentence pair. Cond-NLI can help to provide a full spectrum of information, such as when there are multiple answers to a question each addressing a specific condition, or reviews with different opinions for different conditions. We show that widely-used feature-attribution explanation models are not suitable for finding conditions, especially when sentences are long and are written independently. We propose a simple yet effective model for the NLI task that can successfully extract conditions while not requiring token-level annotations. Our model enhances the interpretability while maintaining comparable accuracy. To evaluate Cond-NLI, we present a token-level annotated dataset BioClaim which contains potentially contradictory claims from the biomedical articles. Experiments show that our model outperforms the full cross-encoder and other baselines in extracting conditions. It also performs on-par with GPT-3 which has an order of magnitude more parameters and trained on a huge amount of data. (1)