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

With an increase of dataset availability, the potential for learning from avariety of data sources has increased. One particular method to improvelearning from multiple data sources is to embed the data source duringtraining. This allows the model to learn generalizable features as well asdistinguishing features between datasets. However, these dataset embeddingshave mostly been used before contextualized transformer-based embeddings wereintroduced in the field of Natural Language Processing. In this work, wecompare two methods to embed datasets in a transformer-based multilingualdependency parser, and perform an extensive evaluation. We show that: 1)embedding the dataset is still beneficial with these models 2) performanceincreases are highest when embedding the dataset at the encoder level 3)unsurprisingly, we confirm that performance increases are highest for smalldatasets and datasets with a low baseline score. 4) we show that training onthe combination of all datasets performs similarly to designing smallerclusters based on language-relatedness.

Parsing with Pretrained Language Models, Multiple Datasets, and Dataset Embeddings