Papers by Minghua Yang
DUET: Joint Exploration of User–Item Profiles in Recommendation System (2026.findings-acl)
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Yue Chen, Yifei Sun, Lu Wang, Fangkai Yang, Pu Zhao, Minjie Hong, Yifei Dong, Minghua He, Nan Hu, Jianjin Zhang, Zhiwei Dai, Yuefeng Zhan, Weihao Han, Hao Sun, Qingwei Lin, Weiwei Deng, Feng Sun, Qi Zhang, Saravan Rajmohan, Dongmei Zhang
| Challenge: | Existing LLMs are opaque and difficult to interpret, resulting in limited interpretability. |
| Approach: | They propose an interaction-aware profile generator that jointly produces user and item profiles conditioned on both user history and item evidence. |
| Outcome: | The proposed model outperforms baselines on three real-world datasets. |
ExeCoder: Empowering Large Language Models with Executability Representation for Code Translation (2025.emnlp-main)
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Minghua He, Yue Chen, Fangkai Yang, Pu Zhao, Wenjie Yin, Yu Kang, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang
| Challenge: | Existing code translation models only learn the contextual semantics of code during pre-training, neglecting executability information closely related to the execution state of the code. |
| Approach: | They propose an LLM specifically designed for code translation called ExeCoder . it uses executability representations such as functional semantics and syntax structures to enhance LLMs' capabilities. |
| Outcome: | The proposed model outperforms existing open-source code translation models on two metrics. |
Enhancing Hyperbolic Knowledge Graph Embeddings via Lorentz Transformations (2024.findings-acl)
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| Challenge: | Existing methods for knowledge graph embedding rely on tangent approximation and are not fully hyperbolic. |
| Approach: | They propose a fully hyperbolic KGE method that represents entities as points in the Lorentz model and represents relations as the intrinsic transformation. |
| Outcome: | The proposed method captures various types of relations including hierarchical structures. |
Missing Modality meets Meta Sampling (M3S): An Efficient Universal Approach for Multimodal Sentiment Analysis with Missing Modality (2022.aacl-main)
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| Challenge: | Existing methods to address missing modalities often assume a particular modality is completely missing due to recording or transmission error. |
| Approach: | They propose a missing modality-based meta-sampling approach for multimodal sentiment analysis with missing modalities . they conduct experiments on IEMOCAP, SIMS and CMU-MOSI datasets . |
| Outcome: | The proposed method significantly improves on existing models with a mixture of missing modalities. |