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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

A novel approach for robust dialogue act detection in a spoken dialogue system is proposed. Shallow representation named partial sentence trees are employed to represent automatic speech recognition outputs. Parsing results of partial sentences can be decomposed into derivation rules, which turn out to be salient features for dialogue act detection. Data-driven dialogue acts are learned via an unsupervised learning algorithm called spectral clustering, in a vector space whose axes correspond to derivation rules. The proposed method is evaluated in a Mandarin spoken dialogue system for tourist-information services. Combined with information obtained from the automatic speech recognition module and from a Markov model on dialogue act sequence, the proposed method achieves a detection accuracy of 85.1%, which is significantly better than the baseline performance of 62.3% using a naïve Bayes classifier. Furthermore, the average number of turns per dialogue session also decreases significantly with the improved detection accuracy.

Robust dialogue act detection based on partial sentence tree, derivation rule, and spectral clustering algorithm