Rationalization is to employ a generator and a predictor to construct a self-explaining NLP model in which the generator selects a subset of human-intelligible pieces of the input text to the following predictor. However, rationalization suffers from two key challenges, i.e., spurious correlation and degeneration, where the predictor overfits the spurious or meaningless pieces solely selected by the not-yet well-trained generator and in turn deteriorates the generator. Although many studies have been proposed to address the two challenges, they are usually designed separately and do not take both of them into account. In this paper, we propose a simple yet effective method named MGR to simultaneously solve the two problems. The key idea of MGR is to employ multiple generators such that the occurrence stability of real pieces is improved and more meaningful pieces are delivered to the predictor. Empirically, we show that MGR improves the F1 score by up to 20.9% as compared to state-of-the-art methods. Codes are available at https://github.com/jugechengzi/Rationalization-MGR .

翻译：可解释性推理旨在通过生成器与预测器构建一种自解释的自然语言处理模型：生成器从输入文本中选取一组人类可理解的片段，并将其传递给后续的预测器。然而，该方法面临两大关键挑战——虚假相关性与退化问题：预测器会过拟合于未充分训练的生成器所选取的虚假或无意义片段，进而导致生成器性能退化。现有研究虽针对这两类挑战分别提出解决方案，但通常未能兼顾二者。本文提出一种简单高效的方法MGR，可同时解决上述两个问题。其核心思想是引入多个生成器，以增强真实片段出现的稳定性，并向预测器传递更有意义的片段。实验结果表明，与现有最优方法相比，MGR的F1分数最高提升20.9%。相关代码已开源至https://github.com/jugechengzi/Rationalization-MGR。