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

This article deals with the utterance-level modalities missing problem with uncertain patterns on emotion recognition in conversation (ERC) task. Present models generally predict the speaker&#x0027;s emotions by its current utterance and context, which is degraded by modality missing considerably. Our work proposes a framework missing-modality robust emotion recognition (M2R2), which trains emotion recognition model with iterative data augmentation by learned common representation. First, a network called party attentive network (PANet) is designed to classify emotions, which tracks all the speakers&#x0027; states and context. Attention mechanism between speaker with other participants and dialogue topic is used to decentralize dependence on multitime and multiparty utterances instead of the possible incomplete one. Moreover, the common representation learning (CRL) problem is defined for modality-missing problem. Data imputation methods improved by the adversarial strategy are used here to construct extra features to augment data. Extensive experiments and case studies validate the effectiveness of our methods over baselines for modality-missing emotion recognition on two different datasets.

M2R2: Missing-Modality Robust Emotion Recognition Framework With Iterative Data Augmentation