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 Plus annual

Presents the access of premium content as premium feature

Premium Content

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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Training monolingual language models for low and mid-resource languages ismade challenging by limited and often inadequate pretraining data. In thisstudy, we propose a novel model conversion strategy to address this issue,adapting high-resources monolingual language models to a new target language.By generalizing over a word translation dictionary encompassing both the sourceand target languages, we map tokens from the target tokenizer to semanticallysimilar tokens from the source language tokenizer. This one-to-many tokenmapping improves tremendously the initialization of the embedding table for thetarget language. We conduct experiments to convert high-resource models to mid-and low-resource languages, namely Dutch and Frisian. These converted modelsachieve a new state-of-the-art performance on these languages across all sortsof downstream tasks. By reducing significantly the amount of data and timerequired for training state-of-the-art models, our novel model conversionstrategy has the potential to benefit many languages worldwide.

Tik-to-Tok: Translating Language Models One Token at a Time: An Embedding Initialization Strategy for Efficient Language Adaptation