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

Task-oriented dialogue (ToD) systems help users execute well-defined tasksacross a variety of domains (e.g., $\textit{flight booking}$ or $\textit{foodordering}$), with their Natural Language Understanding (NLU) components beingdedicated to the analysis of user utterances, predicting users' intents($\textit{Intent Detection}$, ID) and extracting values for informational slots($\textit{Value Extraction}$, VE). In most domains, labelled NLU data isscarce, making sample-efficient learning -- enabled with effective transferparadigms -- paramount. In this work, we introduce SQATIN, a new framework fordialog NLU based on (i) instruction tuning and (ii) question-answering-basedformulation of ID and VE tasks. According to the evaluation on established NLUbenchmarks, SQATIN sets the new state of the art in dialogue NLU, substantiallysurpassing the performance of current models based on standard fine-tuningobjectives in both in-domain training and cross-domain transfer. SQATIN yieldsparticularly large performance gains in cross-domain transfer, owing to thefact that our QA-based instruction tuning leverages similarities betweennatural language descriptions of classes (i.e., slots and intents) acrossdomains.

SQATIN: Supervised Instruction Tuning Meets Question Answering for Improved Dialogue NLU