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

Transliteration is very common on social media, but transliterated text isnot adequately handled by modern neural models for various NLP tasks. In thiswork, we combine data augmentation approaches with a Teacher-Student trainingscheme to address this issue in a cross-lingual transfer setting forfine-tuning state-of-the-art pre-trained multilingual language models such asmBERT and XLM-R. We evaluate our method on transliterated Hindi and Malayalam,also introducing new datasets for benchmarking on real-world scenarios: one onsentiment classification in transliterated Malayalam, and another on crisistweet classification in transliterated Hindi and Malayalam (related to the 2013North India and 2018 Kerala floods). Our method yielded an average improvementof +5.6% on mBERT and +4.7% on XLM-R in F1 scores over their strong baselines.

Cross-Lingual Text Classification of Transliterated Hindi and Malayalam