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

The quality of automatic speech recognition (ASR) is critical to DialogueSystems as ASR errors propagate to and directly impact downstream tasks such aslanguage understanding (LU). In this paper, we propose multi-task neuralapproaches to perform contextual language correction on ASR outputs jointlywith LU to improve the performance of both tasks simultaneously. To measure theeffectiveness of this approach we used a public benchmark, the 2nd DialogueState Tracking (DSTC2) corpus. As a baseline approach, we trained task-specificStatistical Language Models (SLM) and fine-tuned state-of-the-art GeneralizedPre-training (GPT) Language Model to re-rank the n-best ASR hypotheses,followed by a model to identify the dialog act and slots. i) We further trainedranker models using GPT and Hierarchical CNN-RNN models with discriminatorylosses to detect the best output given n-best hypotheses. We extended theseranker models to first select the best ASR output and then identify thedialogue act and slots in an end to end fashion. ii) We also proposed a noveljoint ASR error correction and LU model, a word confusion pointer network(WCN-Ptr) with multi-head self-attention on top, which consumes the wordconfusions populated from the n-best. We show that the error rates of off theshelf ASR and following LU systems can be reduced significantly by 14% relativewith joint models trained using small amounts of in-domain data.

Joint Contextual Modeling for ASR Correction and Language Understanding