With recent advances in natural language processing, rationalization becomes an essential self-explaining diagram to disentangle the black box by selecting a subset of input texts to account for the major variation in prediction. Yet, existing association-based approaches on rationalization cannot identify true rationales when two or more snippets are highly inter-correlated and thus provide a similar contribution to prediction accuracy, so-called spuriousness. To address this limitation, we novelly leverage two causal desiderata, non-spuriousness and efficiency, into rationalization from the causal inference perspective. We formally define a series of probabilities of causation based on a newly proposed structural causal model of rationalization, with its theoretical identification established as the main component of learning necessary and sufficient rationales. The superior performance of the proposed causal rationalization is demonstrated on real-world review and medical datasets with extensive experiments compared to state-of-the-art methods.

翻译：摘要：随着自然语言处理的最新进展，合理化成为一种关键的自解释范式，通过选取输入文本的子集来解释预测中的主要变化，从而解开黑箱之谜。然而，当多个片段高度互相关并对预测精度提供相似贡献时（即所谓的虚假相关性），现有的基于关联的合理化方法无法识别出真正的理由。为解决这一局限性，我们创新性地从因果推断角度引入两个因果必要条件（非虚假性与高效性）至合理化过程中。基于新提出的合理化结构因果模型，我们正式定义了一系列因果概率，并建立其理论识别作为学习必要且充分理由的核心组成部分。通过在真实场景的评论与医学数据集上开展大量实验，证明了所提出的因果合理化方法相较于现有最优方法的卓越性能。