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

Transformer-based models are now predominant in NLP. They outperformapproaches based on static models in many respects. This success has in turnprompted research that reveals a number of biases in the language modelsgenerated by transformers. In this paper we utilize this research on biases toinvestigate to what extent transformer-based language models allow forextracting knowledge about object relations (X occurs in Y; X consists of Z;action A involves using X). To this end, we compare contextualized models withtheir static counterparts. We make this comparison dependent on the applicationof a number of similarity measures and classifiers. Our results are threefold:Firstly, we show that the models combined with the different similaritymeasures differ greatly in terms of the amount of knowledge they allow forextracting. Secondly, our results suggest that similarity measures perform muchworse than classifier-based approaches. Thirdly, we show that, surprisingly,static models perform almost as well as contextualized models -- in some caseseven better.

What do Toothbrushes do in the Kitchen? How Transformers Think our World is Structured