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

Active learning (AL) uses a data selection algorithm to select usefultraining samples to minimize annotation cost. This is now an essential tool forbuilding low-resource syntactic analyzers such as part-of-speech (POS) taggers.Existing AL heuristics are generally designed on the principle of selectinguncertain yet representative training instances, where annotating theseinstances may reduce a large number of errors. However, in an empirical studyacross six typologically diverse languages (German, Swedish, Galician, NorthSami, Persian, and Ukrainian), we found the surprising result that even in anoracle scenario where we know the true uncertainty of predictions, thesecurrent heuristics are far from optimal. Based on this analysis, we pose theproblem of AL as selecting instances which maximally reduce the confusionbetween particular pairs of output tags. Extensive experimentation on theaforementioned languages shows that our proposed AL strategy outperforms otherAL strategies by a significant margin. We also present auxiliary resultsdemonstrating the importance of proper calibration of models, which we ensurethrough cross-view training, and analysis demonstrating how our proposedstrategy selects examples that more closely follow the oracle datadistribution.

Reducing Confusion in Active Learning for Part-Of-Speech Tagging