We have been evaluating text prediction wrong. If the word to predict is 'dog' and we predict cat is 80% likely, dog 20% likely, that costs us accuracy! But really our prediction "cat is 80% likely, dog 20% likely" is better than "boat 80% likely, dog 20% likely", because cat is more similar to dog than boat, we can't only look at the prediction dog. But we can't evaluate like that until we find a good algorithm that first is good at accuracy of exact prediction (I predict cat, and none else), then we do % (I predict cat 80%, dog 20%), then similars, and so on, eventually making the test the same as the algorithm. We can store arithmetic better by making cat and dog share the same space.
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