Papers with disentangled embeddings

1 papers
Explaining Word Embeddings via Disentangled Representation (2020.aacl-main)

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Challenge: Disentangled representations are known to represent interpretable factors in separated dimensions.
Approach: They propose to transform dense word vectors into disentangled embeddings with improved interpretability by encoding polysemous semantics separately.
Outcome: The proposed model can be encoded into multiple sub-embeddings or sub-areas and generates more efficient and effective features for natural language processing.

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