Negation is a common linguistic phenomenon. Yet language models face challenges with negation in many natural language understanding tasks such as question answering and natural language inference. In this paper, we experiment with seamless strategies that incorporate affirmative interpretations (i.e., paraphrases without negation) to make models more robust against negation. Crucially, our affirmative interpretations are obtained automatically. We show improvements with CondaQA, a large corpus requiring reasoning with negation, and five natural language understanding tasks.

翻译：否定是一种常见的语言现象。然而，在问答和自然语言推理等自然语言理解任务中，语言模型在处理否定时仍面临挑战。本文尝试采用无缝策略，通过融入肯定性解释（即不含否定的释义）来增强模型对否定的鲁棒性。关键之处在于，我们的肯定性解释是自动获取的。我们在需要否定推理的大规模数据集CondaQA以及五项自然语言理解任务上验证了方法的改进效果。