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

Text attributes, such as user and product information in product reviews,have been used to improve the performance of sentiment classification models.The de facto standard method is to incorporate them as additional biases in theattention mechanism, and more performance gains are achieved by extending themodel architecture. In this paper, we show that the above method is the leasteffective way to represent and inject attributes. To demonstrate thishypothesis, unlike previous models with complicated architectures, we limit ourbase model to a simple BiLSTM with attention classifier, and instead focus onhow and where the attributes should be incorporated in the model. We propose torepresent attributes as chunk-wise importance weight matrices and consider fourlocations in the model (i.e., embedding, encoding, attention, classifier) toinject attributes. Experiments show that our proposed method achievessignificant improvements over the standard approach and that attentionmechanism is the worst location to inject attributes, contradicting prior work.We also outperform the state-of-the-art despite our use of a simple base model.Finally, we show that these representations transfer well to other tasks. Modelimplementation and datasets are released here:https://github.com/rktamplayo/CHIM.

Rethinking Attribute Representation and Injection for Sentiment Classification