2 citations · 7 across the 14 of their papers we have counts for
14 papers
SURE: Synergistic Uncertainty-aware Reasoning for Multimodal Emotion Recognition in Conversations
Yiqiang Cai, Chengyan Wu, Bolei Ma +4
Multimodal emotion recognition in conversations (MERC) requires integrating multimodal signals while being robust to noise and modeling contextual reasoning. Existing approaches of…
Huntington Disease Automatic Speech Recognition with Biomarker Supervision
Charles L. Wang, Cady Chen, Ziwei Gong +1
Automatic speech recognition (ASR) for pathological speech remains underexplored, especially for Huntington's disease (HD), where irregular timing, unstable phonation, and articula…
Factuality on Demand: Controlling the Factuality-Informativeness Trade-off in Text Generation
Ziwei Gong, Yanda Chen, Julia Hirschberg +4
Large language models (LLMs) encode knowledge with varying degrees of confidence. When responding to queries, models face an inherent trade-off: they can generate responses that ar…
Detecting Mental Manipulation in Speech via Synthetic Multi-Speaker Dialogue
Run Chen, Wen Liang, Ziwei Gong +2
Mental manipulation, the strategic use of language to covertly influence or exploit others, is a newly emerging task in computational social reasoning. Prior work has focused exclu…
CantoASR: Prosody-Aware ASR-LALM Collaboration for Low-Resource Cantonese
Dazhong Chen, Yi-Cheng Lin, Yuchen Huang +5
Automatic speech recognition (ASR) is critical for language accessibility, yet low-resource Cantonese remains challenging due to limited annotated data, six lexical tones, tone san…
Learning More with Less: Self-Supervised Approaches for Low-Resource Speech Emotion Recognition
Ziwei Gong, Pengyuan Shi, Kaan Donbekci +5
Speech Emotion Recognition (SER) has seen significant progress with deep learning, yet remains challenging for Low-Resource Languages (LRLs) due to the scarcity of annotated data.…
Multimodal Emotion Recognition in Conversations: A Survey of Methods, Trends, Challenges and Prospects
Chengyan Wu, Yiqiang Cai, Yang Liu +5
While text-based emotion recognition methods have achieved notable success, real-world dialogue systems often demand a more nuanced emotional understanding than any single modality…
A Review of Incorporating Psychological Theories in LLMs
Zizhou Liu, Ziwei Gong, Lin Ai +5
Psychological insights have long shaped pivotal NLP breakthroughs, from attention mechanisms to reinforcement learning and social modeling. As Large Language Models (LLMs) develop,…
Pragmatics in the Era of Large Language Models: A Survey on Datasets, Evaluation, Opportunities and Challenges
Bolei Ma, Yuting Li, Wei Zhou +7
Understanding pragmatics-the use of language in context-is crucial for developing NLP systems capable of interpreting nuanced language use. Despite recent advances in language tech…
Akan Cinematic Emotions (ACE): A Multimodal Multi-party Dataset for Emotion Recognition in Movie Dialogues
David Sasu, Zehui Wu, Ziwei Gong +5
In this paper, we introduce the Akan Conversation Emotion (ACE) dataset, the first multimodal emotion dialogue dataset for an African language, addressing the significant lack of r…
NovAScore: A New Automated Metric for Evaluating Document Level Novelty
Lin Ai, Ziwei Gong, Harshsaiprasad Deshpande +4
The rapid expansion of online content has intensified the issue of information redundancy, underscoring the need for solutions that can identify genuinely new information. Despite…
CREAM: Comparison-Based Reference-Free ELO-Ranked Automatic Evaluation for Meeting Summarization
Ziwei Gong, Lin Ai, Harshsaiprasad Deshpande +5
Large Language Models (LLMs) have spurred interest in automatic evaluation methods for summarization, offering a faster, more cost-effective alternative to human evaluation. Howeve…
Beyond Silent Letters: Amplifying LLMs in Emotion Recognition with Vocal Nuances
Zehui Wu, Ziwei Gong, Lin Ai +3
Emotion recognition in speech is a challenging multimodal task that requires understanding both verbal content and vocal nuances. This paper introduces a novel approach to emotion…
Multimodal Multi-loss Fusion Network for Sentiment Analysis
Zehui Wu, Ziwei Gong, Jaywon Koo +1
This paper investigates the optimal selection and fusion of feature encoders across multiple modalities and combines these in one neural network to improve sentiment detection. We…