Panosent: A panoptic sextuple extraction benchmark for multimodal conversational aspect-based sentiment analysis
While existing Aspect-based Sentiment Analysis (ABSA) has received extensive effort and
advancement, there are still gaps in defining a more holistic research target seamlessly …
advancement, there are still gaps in defining a more holistic research target seamlessly …
A Survey of Ontology Expansion for Conversational Understanding
In the rapidly evolving field of conversational AI, Ontology Expansion (OnExp) is crucial for
enhancing the adaptability and robustness of conversational agents. Traditional models rely …
enhancing the adaptability and robustness of conversational agents. Traditional models rely …
A unimodal valence-arousal driven contrastive learning framework for multimodal multi-label emotion recognition
Multimodal Multi-Label Emotion Recognition (MMER) aims to identify one or more emotion
categories expressed by an utterance of a speaker. Despite obtaining promising results …
categories expressed by an utterance of a speaker. Despite obtaining promising results …
Event-centric hierarchical hyperbolic graph for multi-hop question answering over knowledge graphs
X Zhu, W Gao, T Li, W Yao, H Deng - Engineering Applications of Artificial …, 2024 - Elsevier
Abstract Question Answering over Knowledge Graphs (KGQA) blends natural language
processing with structured knowledge representation. While much attention of existing …
processing with structured knowledge representation. While much attention of existing …
SpeechEE: A Novel Benchmark for Speech Event Extraction
Event extraction (EE) is a critical direction in the field of information extraction, laying an
important foundation for the construction of structured knowledge bases. EE from text has …
important foundation for the construction of structured knowledge bases. EE from text has …
Multimodal emotion-cause pair extraction with holistic interaction and label constraint
The multimodal emotion-cause pair extraction (MECPE) task aims to detect the emotions,
causes, and emotion-cause pairs from multimodal conversations. Existing methods for this …
causes, and emotion-cause pairs from multimodal conversations. Existing methods for this …
Multimodal Consistency-Based Teacher for Semi-Supervised Multimodal Sentiment Analysis
Multimodal sentiment analysis holds significant importance within the realm of human-
computer interaction. Due to the ease of collecting unlabeled online resources compared to …
computer interaction. Due to the ease of collecting unlabeled online resources compared to …
FacialPulse: An Efficient RNN-based Depression Detection via Temporal Facial Landmarks
Depression is a prevalent mental health disorder that significantly impacts individuals' lives
and well-being. Early detection and intervention are crucial for effective treatment and …
and well-being. Early detection and intervention are crucial for effective treatment and …
Textualized and feature-based models for compound multimodal emotion recognition in the wild
N Richet, S Belharbi, H Aslam, ME Schadt… - arXiv preprint arXiv …, 2024 - arxiv.org
Systems for multimodal emotion recognition (ER) are commonly trained to extract features
from different modalities (eg, visual, audio, and textual) that are combined to predict …
from different modalities (eg, visual, audio, and textual) that are combined to predict …
A twin disentanglement Transformer Network with Hierarchical-Level Feature Reconstruction for robust multimodal emotion recognition
C Li, L Xie, X Wang, H Pan, Z Wang - Expert Systems with Applications, 2025 - Elsevier
In real-world human–computer interaction, the performance of multimodal emotion
recognition models is inevitably affected by random modality feature missing. Thus, robust …
recognition models is inevitably affected by random modality feature missing. Thus, robust …