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Textual representations based on pre-trained language models are key,especially in few-shot learning scenarios. What makes a representation good fortext classification? Is it due to the geometric properties of the space orbecause it is well aligned with the task? We hypothesize the second claim. Totest it, we develop a task alignment score based on hierarchical clusteringthat measures alignment at different levels of granularity. Our experiments ontext classification validate our hypothesis by showing that task alignment canexplain the classification performance of a given representation.

Analyzing Text Representations by Measuring Task Alignment