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

Frame semantic parsing is a complex problem which includes multipleunderlying subtasks. Recent approaches have employed joint learning of subtasks(such as predicate and argument detection), and multi-task learning of relatedtasks (such as syntactic and semantic parsing). In this paper, we exploremulti-task learning of all subtasks with transformer-based models. We show thata purely generative encoder-decoder architecture handily beats the previousstate of the art in FrameNet 1.7 parsing, and that a mixed decoding multi-taskapproach achieves even better performance. Finally, we show that the multi-taskmodel also outperforms recent state of the art systems for PropBank SRL parsingon the CoNLL 2012 benchmark.

Open-Domain Frame Semantic Parsing Using Transformers