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

Intent detection is a key component of modern goal-oriented dialog systemsthat accomplish a user task by predicting the intent of users' text input.There are three primary challenges in designing robust and accurate intentdetection models. First, typical intent detection models require a large amountof labeled data to achieve high accuracy. Unfortunately, in practical scenariosit is more common to find small, unbalanced, and noisy datasets. Secondly, evenwith large training data, the intent detection models can see a differentdistribution of test data when being deployed in the real world, leading topoor accuracy. Finally, a practical intent detection model must becomputationally efficient in both training and single query inference so thatit can be used continuously and re-trained frequently. We benchmark intentdetection methods on a variety of datasets. Our results show that WatsonAssistant's intent detection model outperforms other commercial solutions andis comparable to large pretrained language models while requiring only afraction of computational resources and training data. Watson Assistantdemonstrates a higher degree of robustness when the training and testdistributions differ.

Benchmarking Commercial Intent Detection Services with Practice-Driven Evaluations