Creating more domain-specific versions of common machine learning benchmarks

Standard benchmarks (e.g., for image recognition or natural language inference) are usually quite domain-agnostic (e.g., do not cover data from domains of science and technology).

Having domain-specific versions of standard benchmarks could help both in improving the state-of-the-art models for this domain, and, more generally, help in determining how well certain models perform across different kinds of data.

Impact: High 
Difficulty: Moderate 



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