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

Causal Emotion Entailment (CEE) aims to discover the potential causes behindan emotion in a conversational utterance. Previous works formalize CEE asindependent utterance pair classification problems, with emotion and speakerinformation neglected. From a new perspective, this paper considers CEE in ajoint framework. We classify multiple utterances synchronously to capture thecorrelations between utterances in a global view and propose a Two-StreamAttention Model (TSAM) to effectively model the speaker's emotional influencesin the conversational history. Specifically, the TSAM comprises three modules:Emotion Attention Network (EAN), Speaker Attention Network (SAN), andinteraction module. The EAN and SAN incorporate emotion and speaker informationin parallel, and the subsequent interaction module effectively interchangesrelevant information between the EAN and SAN via a mutual BiAffinetransformation. Extensive experimental results demonstrate that our modelachieves new State-Of-The-Art (SOTA) performance and outperforms baselinesremarkably.

TSAM: A Two-Stream Attention Model for Causal Emotion Entailment