Teaching Machine Comprehension with Compositional Explanations

2 May 2020Qinyuan YeXiao HuangXiang Ren

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... (read more)

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