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 propose SEARNN, a novel training algorithm for recurrent neural networks(RNNs) inspired by the "learning to search" (L2S) approach to structuredprediction. RNNs have been widely successful in structured predictionapplications such as machine translation or parsing, and are commonly trainedusing maximum likelihood estimation (MLE). Unfortunately, this training loss isnot always an appropriate surrogate for the test error: by only maximizing theground truth probability, it fails to exploit the wealth of information offeredby structured losses. Further, it introduces discrepancies between training andpredicting (such as exposure bias) that may hurt test performance. Instead,SEARNN leverages test-alike search space exploration to introduce global-locallosses that are closer to the test error. We first demonstrate improvedperformance over MLE on two different tasks: OCR and spelling correction. Then,we propose a subsampling strategy to enable SEARNN to scale to large vocabularysizes. This allows us to validate the benefits of our approach on a machinetranslation task.

SEARNN: Training RNNs with Global-Local Losses