activity
20192026
most citedLearning to Generate Questions by Learning What not to Generate

105 citations · 183 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CL2026

Hierarchical Acoustic-Semantic Modeling: Modality Separation and Semantic Coherence for Full-Duplex SLMs

Zhenyu Liu, Xuanyu Zhang, Yunxin Li +10

Developing seamless, high-performance, native intelligent full-duplex Spoken Language Models (SLMs) remains a critical challenge and long-standing goal for the speech and NLP commu…

cs.SD2025

UniMoE-Audio: Unified Speech and Music Generation with Dynamic-Capacity MoE

Zhenyu Liu, Yunxin Li, Xuanyu Zhang +13

Recent advances in unified multimodal models indicate a clear trend towards comprehensive content generation. However, the auditory domain remains a significant challenge, with mus…

cs.AI2023★ 2 cited

ConKI: Contrastive Knowledge Injection for Multimodal Sentiment Analysis

Yakun Yu, Mingjun Zhao, Shi-ang Qi +6

Multimodal Sentiment Analysis leverages multimodal signals to detect the sentiment of a speaker. Previous approaches concentrate on performing multimodal fusion and representation…

cs.CV2023★ 2 cited

Search-Map-Search: A Frame Selection Paradigm for Action Recognition

Mingjun Zhao, Yakun Yu, Xiaoli Wang +2

Despite the success of deep learning in video understanding tasks, processing every frame in a video is computationally expensive and often unnecessary in real-time applications. F…

cs.CV2023★ 1 cited

LA3: Efficient Label-Aware AutoAugment

Mingjun Zhao, Shan Lu, Zixuan Wang +2

Automated augmentation is an emerging and effective technique to search for data augmentation policies to improve generalizability of deep neural network training. Most existing wo…

cs.IR2021★ 73 cited

RecGURU: Adversarial Learning of Generalized User Representations for Cross-Domain Recommendation

Chenglin Li, Mingjun Zhao, Huanming Zhang +5

Cross-domain recommendation can help alleviate the data sparsity issue in traditional sequential recommender systems. In this paper, we propose the RecGURU algorithm framework to g…

cs.CL2019★ 105 cited

Learning to Generate Questions by Learning What not to Generate

Bang Liu, Mingjun Zhao, Di Niu +4

Automatic question generation is an important technique that can improve the training of question answering, help chatbots to start or continue a conversation with humans, and prov…