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Copying, often seen as a rote task, unlocks analogical reasoning in transformers by forcing them to attend to the most relevant features.
Self-supervised speech models don't learn all linguistic features at once: the *order* in which they learn depends heavily on the pre-training objective and the level of abstraction of the linguistic feature.
