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> meaningless to talk about the cosine similarity of your sentences without setting out your assumptions about how you will created embeddings from them. I agr
by latency-guy2 3y ago
> meaningless to talk about the cosine similarity of your sentences without setting out your assumptions about how you will created embeddings from them.
I agree, but from generics POV, you have to settle on a few things to compare between models. If you can't, then benchmarks are useless too outside of extremely narrow measures.
I only address structure in the parent, and sure, it can be too generic of a statement by only touching on structure. But I would almost assert structure is still an important feature, and I would almost assert that it is required or otherwise a dominant feature when you want to deliver a product for general use.
I don't think I get too much more incorrect going beyond a few dimensions given this.
- vidarh 3y agoFrom the introduction to the paper: > Discrete entities are often embedded via a learned mapping to dense real-valued vectors in a variety of domains. Already from that point, it is clear that a comparison based on the similarity of the textual version of the sentences is irrelevant to the evaluation in the paper. The paper consistently talk in terms of "learned embeddings" rather than simplistic direct mappings of words.