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

Recently, the retrieval models based on dense representations have beengradually applied in the first stage of the document retrieval tasks, showingbetter performance than traditional sparse vector space models. To obtain highefficiency, the basic structure of these models is Bi-encoder in most cases.However, this simple structure may cause serious information loss during theencoding of documents since the queries are agnostic. To address this problem,we design a method to mimic the queries on each of the documents by aniterative clustering process and represent the documents by multiple pseudoqueries (i.e., the cluster centroids). To boost the retrieval process usingapproximate nearest neighbor search library, we also optimize the matchingfunction with a two-step score calculation procedure. Experimental results onseveral popular ranking and QA datasets show that our model can achievestate-of-the-art results.

Improving Document Representations by Generating Pseudo Query Embeddings for Dense Retrieval