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

In this paper, we analyze several neural network designs (and theirvariations) for sentence pair modeling and compare their performanceextensively across eight datasets, including paraphrase identification,semantic textual similarity, natural language inference, and question answeringtasks. Although most of these models have claimed state-of-the-art performance,the original papers often reported on only one or two selected datasets. Weprovide a systematic study and show that (i) encoding contextual information byLSTM and inter-sentence interactions are critical, (ii) Tree-LSTM does not helpas much as previously claimed but surprisingly improves performance on Twitterdatasets, (iii) the Enhanced Sequential Inference Model is the best so far forlarger datasets, while the Pairwise Word Interaction Model achieves the bestperformance when less data is available. We release our implementations as anopen-source toolkit.

Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question Answering