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

Translation has played a crucial role in improving the performance onmultilingual tasks: (1) to generate the target language data from the sourcelanguage data for training and (2) to generate the source language data fromthe target language data for inference. However, prior works have notconsidered the use of both translations simultaneously. This paper shows thatcombining them can synergize the results on various multilingual sentenceclassification tasks. We empirically find that translation artifacts stylizedby translators are the main factor of the performance gain. Based on thisanalysis, we adopt two training methods, SupCon and MixUp, consideringtranslation artifacts. Furthermore, we propose a cross-lingual fine-tuningalgorithm called MUSC, which uses SupCon and MixUp jointly and improves theperformance. Our code is available at https://github.com/jongwooko/MUSC.

Synergy with Translation Artifacts for Training and Inference in Multilingual Tasks