Train a network to copy its input to its output, and the task sounds trivially easy — the identity function does it perfectly. Force that copying to pass through a narrow bottleneck first, and suddenly the network has to decide what matters enough to keep. That forced compression is the entire idea behind autoencoders.
One-hot encoding a vocabulary treats every word as equally different from every other word — "cat" is exactly as far from "dog" as it is from "bicycle." Word embeddings replace that with geometry: words that appear in similar contexts end up close together in a continuous vector space, and "close" starts to mean "similar" in a way a model can actually use.