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It has become a common pattern in our field: One group introduces a languagetask, exemplified by a dataset, which they argue is challenging enough to serveas a benchmark. They also provide a baseline model for it, which then soon isimproved upon by other groups. Often, research efforts then move on, and thepattern repeats itself. What is typically left implicit is the argumentationfor why this constitutes progress, and progress towards what. In this paper, wetry to step back for a moment from this pattern and work out possibleargumentations and their parts.

Targeting the Benchmark: On Methodology in Current Natural Language Processing Research