Papers with static embedding predecessors

1 papers
Interpreting Pretrained Contextualized Representations via Reductions to Static Embeddings (2020.acl-main)

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Challenge: Contextualized representations have become the default for downstream NLP applications.
Approach: They propose a method for converting from contextualized representations to static lookup-table embeddings and apply it to 5 popular pretrained models and 9 sets of pretrained weights.
Outcome: The proposed methods show that pooling over many contexts significantly improves representational quality under intrinsic evaluation.

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