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

Transfer Learning (TL) plays a crucial role when a given dataset hasinsufficient labeled examples to train an accurate model. In such scenarios,the knowledge accumulated within a model pre-trained on a source dataset can betransferred to a target dataset, resulting in the improvement of the targetmodel. Though TL is found to be successful in the realm of image-basedapplications, its impact and practical use in Natural Language Processing (NLP)applications is still a subject of research. Due to their hierarchicalarchitecture, Deep Neural Networks (DNN) provide flexibility and customizationin adjusting their parameters and depth of layers, thereby forming an apt areafor exploiting the use of TL. In this paper, we report the results andconclusions obtained from extensive empirical experiments using a ConvolutionalNeural Network (CNN) and try to uncover thumb rules to ensure a successfulpositive transfer. In addition, we also highlight the flawed means that couldlead to a negative transfer. We explore the transferability of various layersand describe the effect of varying hyper-parameters on the transferperformance. Also, we present a comparison of accuracy value and model sizeagainst state-of-the-art methods. Finally, we derive inferences from theempirical results and provide best practices to achieve a successful positivetransfer.

A Practitioners' Guide to Transfer Learning for Text Classification using Convolutional Neural Networks