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

Unknown intent detection aims to identify the out-of-distribution (OOD)utterance whose intent has never appeared in the training set. In this paper,we propose using energy scores for this task as the energy score istheoretically aligned with the density of the input and can be derived from anyclassifier. However, high-quality OOD utterances are required during thetraining stage in order to shape the energy gap between OOD and in-distribution(IND), and these utterances are difficult to collect in practice. To tacklethis problem, we propose a data manipulation framework to Generate high-qualityOOD utterances with importance weighTs (GOT). Experimental results show thatthe energy-based detector fine-tuned by GOT can achieve state-of-the-artresults on two benchmark datasets.

Energy-based Unknown Intent Detection with Data Manipulation