Ling-CL: Understanding NLP Models through Linguistic Curricula
Submission Type: Regular Long Paper
Submission Track: Machine Learning for NLP
Submission Track 2: Linguistic Theories, Cognitive Modeling, and Psycholinguistics
Keywords: linguistic index, curriculum learning
Abstract: 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. Through the evaluation of 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.
Submission Number: 4477
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