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Unsupervised embedding quality evaluation

Posted on One min read

paper · pdf

TL;DR

We can approximately know the quality of a given set of embeddings.

In this paper:

  • We identify three different perspectives on evaluation of embedding quality in unsupervised manner and introduce four metrics based on these perspectives.
  • We experimentally study two novel settings for embedding quality evaluation, showing that standard metrics often fail when shallow models are being studied.
  • We conduct a study on computational stability of all metrics and identify the minimum viable sample sizes.