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Found 5 papers, 1 papers with code
基于篇章结构攻击的阅读理解任务探究(Analysis of Reading Comprehension Tasks based on passage structure attacks)
“本文实验发现, 段落顺序会影响人类阅读理解效果;而打乱段落或句子顺序, 对BERT、ALBERT和RoBERTa三种人工神经网络模型的阅读理解答题几乎没有影响。打乱词序后, 人的阅读理解水平低于三个模型, 但人和模型的答题情况高于随机水平, 这说明人比人工神经网络对词序更敏感, 但人与模型可以在单词乱序的情况下答题。综上, 人与人工神经网络在正常阅读的情况下回答阅读理解问题的正确率相当, 但两者对篇章结构及语序的依赖程度不同。”
Complex Mapping between Neural Response Frequency and Linguistic Units in Natural Speech
When listening to connected speech, human brain can extract multiple levels of linguistic units, such as syllables, words, and sentences.
Human Attention during Goal-directed Reading Comprehension Relies on Task Optimization
Furthermore, when readers scan a passage without a question in mind, their reading time is predicted by DNNs optimized for a word prediction task.
PALRACE: Reading Comprehension Dataset with Human Data and Labeled Rationales
Each passage was read by at least 26 human readers, who labeled their rationales to answer the question.
Using Adversarial Attacks to Reveal the Statistical Bias in Machine Reading Comprehension Models
We apply the method to the RACE dataset, for which the answer to each MRC question is selected from 4 options.
