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

While natural language processing systems often focus on a single language,multilingual transfer learning has the potential to improve performance,especially for low-resource languages. We introduce XLDA, cross-lingual dataaugmentation, a method that replaces a segment of the input text with itstranslation in another language. XLDA enhances performance of all 14 testedlanguages of the cross-lingual natural language inference (XNLI) benchmark.With improvements of up to $4.8\%$, training with XLDA achievesstate-of-the-art performance for Greek, Turkish, and Urdu. XLDA is in contrastto, and performs markedly better than, a more naive approach that aggregatesexamples in various languages in a way that each example is solely in onelanguage. On the SQuAD question answering task, we see that XLDA provides a$1.0\%$ performance increase on the English evaluation set. Comprehensiveexperiments suggest that most languages are effective as cross-lingualaugmentors, that XLDA is robust to a wide range of translation quality, andthat XLDA is even more effective for randomly initialized models than forpretrained models.

XLDA: Cross-Lingual Data Augmentation for Natural Language Inference and Question Answering