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：可解释性是一种通过生成器与预测器构建自解释NLP模型的方法，其中生成器从输入文本中选取一组人类可理解的片段供后续预测器使用。然而，该方法面临两个关键挑战——虚假相关性与退化问题：预测器过度拟合尚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恶化。尽管已有诸多研究分别针对这两个挑战提出解决方案，但通常未将二者联合考虑。本文提出一种名为MGR的简洁高效方法，可同时解决上述两个问题。其核心思想在于采用多个生成器，从而提升真实片段出现的稳定性，并将更多有意义的片段传递给预测器。实验表明，与当前最优方法相比，MGR将F1分数提升最高达20.9%。代码开源地址：https://github.com/jugechengzi/Rationalization-MGR。