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• W3155128949 abstract "Post-processing of static embedding has beenshown to improve their performance on both lexical and sequence-level tasks. However, post-processing for contextualized embeddings is an under-studied problem. In this work, we question the usefulness of post-processing for contextualized embeddings obtained from different layers of pre-trained language models. More specifically, we standardize individual neuron activations using z-score, min-max normalization, and by removing top principle components using the all-but-the-top method. Additionally, we apply unit length normalization to word representations. On a diverse set of pre-trained models, we show that post-processing unwraps vital information present in the representations for both lexical tasks (such as word similarity and analogy)and sequence classification tasks. Our findings raise interesting points in relation to theresearch studies that use contextualized representations, and suggest z-score normalization as an essential step to consider when using them in an application." @default.
• W3155128949 created "2021-04-26" @default.
• W3155128949 creator A5002245931 @default.
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• W3155128949 creator A5042954793 @default.
• W3155128949 creator A5053436179 @default.
• W3155128949 date "2021-04-15" @default.
• W3155128949 modified "2023-09-27" @default.
• W3155128949 title "Effect of Post-processing on Contextualized Word Representations." @default.
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• W3155128949 hasPublicationYear "2021" @default.
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