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We employ a characterization of linguistic complexity from psycholinguisticand language acquisition research to develop data-driven curricula tounderstand the underlying linguistic knowledge that models learn to address NLPtasks. The novelty of our approach is in the development of linguisticcurricula derived from data, existing knowledge about linguistic complexity,and model behavior during training. By analyzing several benchmark NLPdatasets, our curriculum learning approaches identify sets of linguisticmetrics (indices) that inform the challenges and reasoning required to addresseach task. Our work will inform future research in all NLP areas, allowinglinguistic complexity to be considered early in the research and developmentprocess. In addition, our work prompts an examination of gold standards andfair evaluation in NLP.

Ling-CL: Understanding NLP Models through Linguistic Curricula