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.

本文提出了一种可解释自然语言理解的框架，使用一小部分人类注释的解释进行训练，并采用变分 EM 方法进行优化，同时提出了基于解释的自训练方法，在两个自然语言理解任务上进行实验，证明了该框架不仅可以在监督和半监督设置下进行有效的预测，还可以生成良好的自然语言解释。