Invariance and identifiability issues for word embeddings

NeurIPS 2019 Rachel CarringtonKarthik BharathSimon Preston

Word embeddings are commonly obtained as optimizers of a criterion function $f$ of a text corpus, but assessed on word-task performance using a different evaluation function $g$ of the test data. We contend that a possible source of disparity in performance on tasks is the incompatibility between classes of transformations that leave $f$ and $g$ invariant... (read more)

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