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

This paper describes two supervised baseline systems for the Story Cloze TestShared Task (Mostafazadeh et al., 2016a). We first build a classifier usingfeatures based on word embeddings and semantic similarity computation. Wefurther implement a neural LSTM system with different encoding strategies thattry to model the relation between the story and the provided endings. Ourexperiments show that a model using representation features based on averageword embedding vectors over the given story words and the candidate endingsentences words, joint with similarity features between the story and candidateending representations performed better than the neural models. Our best modelachieves an accuracy of 72.42, ranking 3rd in the official evaluation.

Story Cloze Ending Selection Baselines and Data Examination