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

We propose a new active learning (AL) framework, Active Learning++, which canutilize an annotator's labels as well as its rationale. Annotators can providetheir rationale for choosing a label by ranking input features based on theirimportance for a given query. To incorporate this additional input, we modifiedthe disagreement measure for a bagging-based Query by Committee (QBC) samplingstrategy. Instead of weighing all committee models equally to select the nextinstance, we assign higher weight to the committee model with higher agreementwith the annotator's ranking. Specifically, we generated a featureimportance-based local explanation for each committee model. The similarityscore between feature rankings provided by the annotator and the local modelexplanation is used to assign a weight to each corresponding committee model.This approach is applicable to any kind of ML model using model-agnostictechniques to generate local explanation such as LIME. With a simulation study,we show that our framework significantly outperforms a QBC based vanilla ALframework.

Active Learning++: Incorporating Annotator's Rationale using Local Model Explanation