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

Sequence-to-sequence (S2S) pre-training using large monolingual data is knownto improve performance for various S2S NLP tasks in low-resource settings.However, large monolingual corpora might not always be available for thelanguages of interest (LOI). To this end, we propose to exploit monolingualcorpora of other languages to complement the scarcity of monolingual corporafor the LOI. A case study of low-resource Japanese-English neural machinetranslation (NMT) reveals that leveraging large Chinese and French monolingualcorpora can help overcome the shortage of Japanese and English monolingualcorpora, respectively, for S2S pre-training. We further show how to utilizescript mapping (Chinese to Japanese) to increase the similarity between the twomonolingual corpora leading to further improvements in translation quality.Additionally, we propose simple data-selection techniques to be used prior topre-training that significantly impact the quality of S2S pre-training. Anempirical comparison of our proposed methods reveals that leveraging assistinglanguage monolingual corpora, data selection and script mapping are extremelyimportant for NMT pre-training in low-resource scenarios.

Pre-training via Leveraging Assisting Languages and Data Selection for Neural Machine Translation