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

Emotion Recognition in Conversation (ERC) has been widely studied due to itsimportance in developing emotion-aware empathetic machines. The rise ofpre-trained language models (PLMs) has further pushed the limit of ERCperformance. However, most recent works on ERC using PLMs are heavilydata-driven, and requires fine-tuning the entire PLMs. To improve both sampleand computational efficiency, we propose a derivative-free optimization methodcalled Cross-Task Prompt Tuning (CTPT) for few-shot conversational emotionrecognition. Unlike existing methods that learn independent knowledge fromindividual tasks, CTPT leverages sharable cross-task knowledge by exploitingexternal knowledge from other source tasks to improve learning performanceunder the few-shot setting. Moreover, CTPT only needs to optimize a vectorunder the low intrinsic dimensionality without gradient, which is highlyparameter-efficient compared with existing approaches. Experiments on fivedifferent contextual conversation datasets demonstrate that our CTPT method hassuperior results on both few-shot scenarios and zero-shot transfers.

Efficient Cross-Task Prompt Tuning for Few-Shot Conversational Emotion Recognition