New paper highlights "Annotation Scarcity Paradox" in low-resource NLP evaluation

A new paper published on arXiv discusses the "Annotation Scarcity Paradox" in low-resource Natural Language Processing (NLP). The paper argues that while NLP models have advanced rapidly, the human infrastructure needed for their evaluation is lagging behind. This gap, exacerbated by undercompensated labor and data issues, threatens the validity of reported progress. The authors propose a shift towards community-embedded evaluation with data sovereignty and shared ownership to address these challenges. AI

IMPACT Highlights a critical bottleneck in evaluating low-resource NLP models, potentially impacting the reliability of AI progress claims.

RANK_REASON The cluster contains a research paper discussing a novel concept and its implications for a specific field. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• Annotation Scarcity Paradox
• arXiv
• NLP
• Vukosi Marivate

• paper
• other

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