Teaching Machine Comprehension with Compositional Explanations

Advances in extractive machine reading comprehension (MRC) rely heavily on the collection of large scale human-annotated training data (in the form of "question-paragraph-answer span"). A single question-answer example provides limited supervision, while an explanation in natural language describing human's deduction process may generalize to many other questions that share similar solution patterns. In this paper, we focus on "teaching" machines on reading comprehension with (a small number of) natural language explanations. We propose a data augmentation framework that exploits the compositional nature of explanations to rapidly create pseudo-labeled data for training downstream MRC models. Structured variables and rules are extracted from each explanation and formulated into neural module teacher, which employs softened neural modules and combinatorial search to handle linguistic variations and overcome sparse coverage. The proposed work is particularly effective when limited annotation effort is available, and achieved a practicable F1 score of 59.80% with supervision from 52 explanations on the SQuAD dataset.