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

Translation-based AMR parsers have recently gained popularity due to theirsimplicity and effectiveness. They predict linearized graphs as free texts,avoiding explicit structure modeling. However, this simplicity neglectsstructural locality in AMR graphs and introduces unnecessary tokens torepresent coreferences. In this paper, we introduce new target forms of AMRparsing and a novel model, CHAP, which is equipped with causal hierarchicalattention and the pointer mechanism, enabling the integration of structuresinto the Transformer decoder. We empirically explore various alternativemodeling options. Experiments show that our model outperforms baseline modelson four out of five benchmarks in the setting of no additional data.

AMR Parsing with Causal Hierarchical Attention and Pointers