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

Large-scale pre-trained language models (PLMs) bring new opportunities tochallenging problems, especially those that need high-level intelligence, suchas the math word problem (MWPs). However, directly applying existing PLMs toMWPs can fail as the generation process lacks sufficient supervision and thuslacks fast adaptivity as humans. We notice that human reasoning has a dualreasoning framework that consists of an immediate reaction system (system 1)and a delicate reasoning system (system 2), where the entire reasoning isdetermined by their interaction. This inspires us to develop a cooperativereasoning-induced PLM for solving MWPs, called Cooperative Reasoning (CoRe),resulting in a human-like reasoning architecture with system 1 as the generatorand system 2 as the verifier. In our approach, the generator is responsible forgenerating reasoning paths, and the verifiers are used to supervise theevaluation in order to obtain reliable feedback for the generator. We evaluateour CoRe framework on several mathematical reasoning datasets and achievedecent improvement over state-of-the-art methods, up to 9.6% increase over bestbaselines. Our codes are available at https://github.com/TianHongZXY/CoRe

Solving Math Word Problems via Cooperative Reasoning induced Language Models