Before revision: 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.
After revision: 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) triples. In contrast, humans are typically able to generalize with only a few examples, relying on deeper underlying world knowledge, linguistic sophistication, and/or simply superior deductive powers . 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.