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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Unsupervised representation learning of words from large multilingual corporais useful for downstream tasks such as word sense disambiguation, semantic textsimilarity, and information retrieval. The representation precision oflog-bilinear fastText models is mostly due to their use of subword information.In previous work, the optimization of fastText's subword sizes has not beenfully explored, and non-English fastText models were trained using subwordsizes optimized for English and German word analogy tasks. In our work, we findthe optimal subword sizes on the English, German, Czech, Italian, Spanish,French, Hindi, Turkish, and Russian word analogy tasks. We then propose asimple n-gram coverage model and we show that it predicts better-than-defaultsubword sizes on the Spanish, French, Hindi, Turkish, and Russian word analogytasks. We show that the optimization of fastText's subword sizes matters andresults in a 14% improvement on the Czech word analogy task. We also show thatexpensive parameter optimization can be replaced by a simple n-gram coveragemodel that consistently improves the accuracy of fastText models on the wordanalogy tasks by up to 3% compared to the default subword sizes, and that it iswithin 1% accuracy of the optimal subword sizes.

One Size Does Not Fit All: Finding the Optimal Subword Sizes for FastText Models across Languages