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

CommonsenseQA is a task in which a correct answer is predicted throughcommonsense reasoning with pre-defined knowledge. Most previous works haveaimed to improve the performance with distributed representation withoutconsidering the process of predicting the answer from the semanticrepresentation of the question. To shed light upon the semantic interpretationof the question, we propose an AMR-ConceptNet-Pruned (ACP) graph. The ACP graphis pruned from a full integrated graph encompassing Abstract MeaningRepresentation (AMR) graph generated from input questions and an externalcommonsense knowledge graph, ConceptNet (CN). Then the ACP graph is exploitedto interpret the reasoning path as well as to predict the correct answer on theCommonsenseQA task. This paper presents the manner in which the commonsensereasoning process can be interpreted with the relations and concepts providedby the ACP graph. Moreover, ACP-based models are shown to outperform thebaselines.

I Know What You Asked: Graph Path Learning using AMR for Commonsense Reasoning