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

U.S. Federal Regulators receive over one million comment letters each yearfrom businesses, interest groups, and members of the public, all advocating forchanges to proposed regulations. These comments are believed to havewide-ranging impacts on public policy. However, measuring the impact ofspecific comments is challenging because regulators are required to respond tocomments but they do not have to specify which comments they are addressing. Inthis paper, we propose a simple yet effective solution to this problem by usingan iterative contrastive method to train a neural model aiming for matchingtext from public comments to responses written by regulators. We demonstratethat our proposal substantially outperforms a set of selected text-matchingbaselines on a human-annotated test set. Furthermore, it delivers performancecomparable to the most advanced gigantic language model (i.e., GPT-4), and ismore cost-effective when handling comments and regulator responses matching inlarger scale.

Tracing Influence at Scale: A Contrastive Learning Approach to Linking Public Comments and Regulator Responses