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Since sport commentaries are a fairly restricted domain, and the language does not vary much, I would go for a canned text approach.
Analyse what kind of events you get, and what variables you're dealing with. Then write some template sentences with placeholders for the variables. The more you write for the same data, the more varied your text will be. You ...
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For deep learning models, embedding vectors have become the standard way of encoding text features almost immediately after their introduction.
The reason for this is that neural networks work with data encoded with continuous values ranging from 0 to 1 (or sometimes from -1 to 1). Bag of Words and TF-IDF can be modified to produce values in this range, but ...
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Caveat: I am not a native English speaker (but French). And mostly interested in symbolic artificial intelligence (the topic of my PhD thesis defended in 1990; see books by Jacques Pitrat)
Encoding is related to decoding. Most of the time, if you encode something A into some other thing B, you can "decode" B to get back A.
Otherwise, you would just ...
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From my experience with reading papers and books, I think these two terms are sometimes used interchangeably.
As you also point out, an encoder (in an auto-encoder) also may also learn some "semantics" of the inputs in order to produce the latent space. However, the way encoders are trained may not produce embeddings, with similar properties to e.g....
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