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

通过分析多个基准 NLP 数据集，我们的课程学习方法识别到了一系列语言度量（指标），这些指标揭示了每个任务所需的挑战和推理，从而使得我们的工作在所有 NLP 领域中提供了未来研究的参考，并且在研究开发过程中早期考虑了语言复杂性。此外，我们的工作促使 NLP 领域对黄金标准和公平评估进行审视。