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Pre-trained language models achieves high performance on machine readingcomprehension (MRC) tasks but the results are hard to explain. An appealingapproach to make models explainable is to provide rationales for its decision.To investigate whether human rationales can further improve current models andto facilitate supervised learning of human rationales, here we present PALRACE(Pruned And Labeled RACE), a new MRC dataset with human labeled rationales for800 passages selected from the RACE dataset. We further classified the questionto each passage into 6 types. Each passage was read by at least 26 humanreaders, who labeled their rationales to answer the question. It isdemonstrated that models such as RoBERTa-large outperforms human readers in all6 types of questions, including inference questions, but its performance can befurther improved when having access to the human rationales. Simpler models andpre-trained models that are not fine-tuned based on the task benefit more fromhuman rationales, and their performance can be boosted by more than 30% byrationales. With access to human rationales, a simple model based on the GloVeword embedding can reach the performance of BERT-base.

PALRACE: Reading Comprehension Dataset with Human Data and Labeled Rationales