Recently generating natural language explanations has shown very promising results in not only offering interpretable explanations but also providing additional information and supervision for prediction. However, existing approaches usually require a large set of human annotated explanations for training while collecting a large set of explanations is not only time consuming but also expensive. In this paper, we develop a general framework for interpretable natural language understanding that requires only a small set of human annotated explanations for training. Our framework treats natural language explanations as latent variables that model the underlying reasoning process of a neural model. We develop a variational EM framework for optimization where an explanation generation module and an explanation-augmented prediction module are alternatively optimized and mutually enhance each other. Moreover, we further propose an explanation-based self-training method under this framework for semi-supervised learning. It alternates between assigning pseudo-labels to unlabeled data and generating new explanations to iteratively improve each other. Experiments on two natural language understanding tasks demonstrate that our framework can not only make effective predictions in both supervised and semi-supervised settings, but also generate good natural language explanation.
翻译:最近产生的自然语言解释显示出非常有希望的结果,不仅提供了可解释的解释,而且还提供了额外的信息和预测监督。然而,现有方法通常需要大量的人类附加说明的培训解释,而收集大量解释不仅耗时而且昂贵。在本文件中,我们为可解释的自然语言理解制定了一个总框架,只需要少量人类附加说明的培训解释。我们的框架将自然语言解释作为潜在的变量,以模拟神经模型的基本推理过程。我们为优化开发了一个可变的EM框架,其中解释生成模块和解释增强的预测模块可以相互优化和相互加强。此外,我们在此框架内进一步提议一种基于解释的自我培训方法,用于半超常学习。在给无标签数据分配假标签和产生新解释以相互迭代改进之间作出交替选择。在两种自然语言理解任务上进行的实验表明,我们的框架不仅可以在受监督和半超常控制的环境下做出有效的预测,而且还可以产生良好的自然语言解释。