Towards More Robust NLP System Evaluation: Handling Missing Scores in Benchmarks

Understand

The evaluation of natural language processing (NLP) systems is crucial for advancing the field, but current benchmarking approaches often assume that all systems have scores available for all tasks, which is not always practical.

• In reality, several factors such as the cost of running baseline, private systems, computational limitations, or incomplete data may prevent some systems from being evaluated on entire tasks.
• This paper formalize an existing problem in NLP research: benchmarking when some systems scores are missing on the task, and proposes a novel approach to address it.
• Our method utilizes a compatible partial ranking approach to impute missing data, which is then aggregated using the Borda count method.