While large pre-trained language models (PLM) have shown their great skills at solving discriminative tasks, a significant gap remains when compared with humans for explanation-related tasks. Among them, explaining the reason why a statement is wrong (e.g., against commonsense) is incredibly challenging. The major difficulty is finding the conflict point, where the statement contradicts our real world. This paper proposes Neon, a two-phrase, unsupervised explanation generation framework. Neon first generates corrected instantiations of the statement (phase I), then uses them to prompt large PLMs to find the conflict point and complete the explanation (phase II). We conduct extensive experiments on two standard explanation benchmarks, i.e., ComVE and e-SNLI. According to both automatic and human evaluations, Neon outperforms baselines, even for those with human-annotated instantiations. In addition to explaining a negative prediction, we further demonstrate that Neon remains effective when generalizing to different scenarios.

翻译：虽然经过培训的大型语言模式(PLM)在解决歧视性任务方面表现出了巨大的技能,但在与人类相比,在解释相关任务方面仍然存在着巨大的差距。其中,解释声明错误的原因(如反对常识)是极其艰巨的。主要困难在于找到冲突点,而声明与我们的真实世界相矛盾。本文提出了一个两句话、不受监督的解释生成框架。Neon首先对声明进行纠正的回馈(第一阶段),然后利用它们促使大型PLM找出冲突点并完成解释(第二阶段)。我们广泛试验了两个标准解释基准,即COMVE和电子SNLI。根据自动和人类评价,Neon 超越基线,甚至那些带有人类注释的速记。除了解释负面预测外,我们还进一步证明Neon在概括不同情景时依然有效。