Before revision:  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 explanationson the SQuAD dataset .
After revision:  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 modules are jointly optimized with the MRC model to improve final performance. On the SQuAD dataset, our proposed method achieves 70.14\% F1 score of 59.80\% with supervision from 52 explanationson the SQuAD dataset .