首页|Text Difficulty Study:Do Machines Behave the Same as Humans Regarding Text Difficulty?

Text Difficulty Study:Do Machines Behave the Same as Humans Regarding Text Difficulty?

扫码查看
原文链接
  • NETL
  • NSTL
  • 万方数据
With the emergence of pre-trained models,current neural networks are able to give task performance that is comparable to humans.However,we know little about the fundamental working mechanism of pre-trained models in which we do not know how they approach such performance and how the task is solved by the model.For example,given a task,human learns from easy to hard,where-as the model learns randomly.Undeniably,difficulty-insensitive learning leads to great success in natural language processing(NLP),but little attention has been paid to the effect of text difficulty in NLP.We propose a human learning matching index(HLM Index)to investigate the effect of text difficulty.Experiment results show:1)LSTM gives more human-like learning behavior than BERT.Addi-tionally,UID-SuperLinear gives the best evaluation of text difficulty among four text difficulty criteria.Among nine tasks,some tasks'performance is related to text difficulty,whereas others are not.2)Model trained on easy data performs best in both easy and medium test data,whereas trained on hard data only performs well on hard test data.3)Train the model from easy to hard,leading to quicker convergence.

Cognition inspired natural language processingpsycholinguisticsexplainabilitytext difficultycurriculum learning

Bowen Chen、Xiao Ding、Yi Zhao、Bo Fu、Tingmao Lin、Bing Qin、Ting Liu

展开 >

Research Center for Social Computing and Information Retrieval,Harbin Institute of Technology,Harbin 150001,China

Fundamental Technology Center,China Construction Bank Financial Technology Co.,Ltd.,Beijing 100033,China

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of Heilongjiang Province,ChinaIndustry-University-Research Innovation Foundation of China University

U22B205962176079YQ 2022F0052021ITA05009

2024

10.1007/s11633-023-1424-x

机器智能研究(英文)
中国科学院自动化所

机器智能研究(英文)

CSTPCDEI
影响因子：0.49
ISSN：2731-538X
年,卷(期)：2024.21(2)
  • 37