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

Large, human-annotated datasets are central to the development of naturallanguage processing models. Collecting these datasets can be the mostchallenging part of the development process. We address this problem byintroducing a general purpose technique for ``simulation-to-real'' transfer inlanguage understanding problems with a delimited set of target behaviors,making it possible to develop models that can interpret natural utteranceswithout natural training data. We begin with a synthetic data generationprocedure, and train a model that can accurately interpret utterances producedby the data generator. To generalize to natural utterances, we automaticallyfind projections of natural language utterances onto the support of thesynthetic language, using learned sentence embeddings to define a distancemetric. With only synthetic training data, our approach matches or outperformsstate-of-the-art models trained on natural language data in several domains.These results suggest that simulation-to-real transfer is a practical frameworkfor developing NLP applications, and that improved models for transfer mightprovide wide-ranging improvements in downstream tasks.

Unnatural Language Processing: Bridging the Gap Between Synthetic and Natural Language Data