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[D] any principled reason for cross entropy instead of L2 in language modelling? (more details in post)

Is there any principled reason for doing softmax and cross entropy for the loss in for example transformers, rather than doing L2 over the target embeddings and the output from the model?

When the output from your model by necessity is a dot product such as in shallow models I understand why you need to do cross entropy loss. But for models such as rnns and some variants of transformers wouldn’t L2 loss directly on the desired embedding and output work as well or better?

submitted by /u/mesmer_adama
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.