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

RST-based discourse parsing is an important NLP task with numerous downstreamapplications, such as summarization, machine translation and opinion mining. Inthis paper, we demonstrate a simple, yet highly accurate discourse parser,incorporating recent contextual language models. Our parser establishes the newstate-of-the-art (SOTA) performance for predicting structure and nuclearity ontwo key RST datasets, RST-DT and Instr-DT. We further demonstrate thatpretraining our parser on the recently available large-scale "silver-standard"discourse treebank MEGA-DT provides even larger performance benefits,suggesting a novel and promising research direction in the field of discourseanalysis.

Unleashing the Power of Neural Discourse Parsers -- A Context and Structure Aware Approach Using Large Scale Pretraining