English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We re-evaluate the standard practice of sharing weights between input andoutput embeddings in state-of-the-art pre-trained language models. We show thatdecoupled embeddings provide increased modeling flexibility, allowing us tosignificantly improve the efficiency of parameter allocation in the inputembedding of multilingual models. By reallocating the input embeddingparameters in the Transformer layers, we achieve dramatically betterperformance on standard natural language understanding tasks with the samenumber of parameters during fine-tuning. We also show that allocatingadditional capacity to the output embedding provides benefits to the model thatpersist through the fine-tuning stage even though the output embedding isdiscarded after pre-training. Our analysis shows that larger output embeddingsprevent the model's last layers from overspecializing to the pre-training taskand encourage Transformer representations to be more general and moretransferable to other tasks and languages. Harnessing these findings, we areable to train models that achieve strong performance on the XTREME benchmarkwithout increasing the number of parameters at the fine-tuning stage.

Rethinking embedding coupling in pre-trained language models