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Reading comprehension is a challenging task in natural language processingand requires a set of skills to be solved. While current approaches focus onsolving the task as a whole, in this paper, we propose to use a neural network`skill' transfer approach. We transfer knowledge from several lower-levellanguage tasks (skills) including textual entailment, named entity recognition,paraphrase detection and question type classification into the readingcomprehension model.  We conduct an empirical evaluation and show that transferring language skillknowledge leads to significant improvements for the task with much fewer stepscompared to the baseline model. We also show that the skill transfer approachis effective even with small amounts of training data. Another finding of thiswork is that using token-wise deep label supervision for text classificationimproves the performance of transfer learning.

Neural Skill Transfer from Supervised Language Tasks to Reading Comprehension