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

We introduce and describe the results of a novel shared task on banditlearning for machine translation. The task was organized jointly by Amazon andHeidelberg University for the first time at the Second Conference on MachineTranslation (WMT 2017). The goal of the task is to encourage research onlearning machine translation from weak user feedback instead of humanreferences or post-edits. On each of a sequence of rounds, a machinetranslation system is required to propose a translation for an input, andreceives a real-valued estimate of the quality of the proposed translation forlearning. This paper describes the shared task's learning and evaluation setup,using services hosted on Amazon Web Services (AWS), the data and evaluationmetrics, and the results of various machine translation architectures andlearning protocols.

A Shared Task on Bandit Learning for Machine Translation