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

For high-resource languages like English, text classification is awell-studied task. The performance of modern NLP models easily achieves anaccuracy of more than 90% in many standard datasets for text classification inEnglish (Xie et al., 2019; Yang et al., 2019; Zaheer et al., 2020). However,text classification in low-resource languages is still challenging due to thelack of annotated data. Although methods like weak supervision andcrowdsourcing can help ease the annotation bottleneck, the annotations obtainedby these methods contain label noise. Models trained with label noise may notgeneralize well. To this end, a variety of noise-handling techniques have beenproposed to alleviate the negative impact caused by the errors in theannotations (for extensive surveys see (Hedderich et al., 2021; Algan & Ulusoy,2021)). In this work, we experiment with a group of standard noisy-handlingmethods on text classification tasks with noisy labels. We study both simulatednoise and realistic noise induced by weak supervision. Moreover, we findtask-adaptive pre-training techniques (Gururangan et al., 2020) are beneficialfor learning with noisy labels.

Task-Adaptive Pre-Training for Boosting Learning With Noisy Labels: A Study on Text Classification for African Languages