Papers by Chuang Han
DART: Disambiguation-Aware Reasoning for Video-guided Machine Translation (2026.acl-long)
Copied to clipboard
| Challenge: | Video-guided Machine Translation (VMT) uses short video clips to enhance translation quality, but many samples are text-sufficient. |
| Approach: | They propose a framework that integrates multimodal large language models’ multimodal reasoning into video-guided machine translation by using a pipeline for constructing training data based on multimodal relevance to translation. |
| Outcome: | The proposed framework improves multimodal information utilization in video-guided machine translation, yielding gains in translation quality and computational efficiency. |
SHIFT: Selected Helpful Informative Frame for Video-guided Machine Translation (2025.emnlp-main)
Copied to clipboard
| Challenge: | Video-guided machine translation (VMT) aims to improve translation quality by integrating contextual information from paired short video clips. |
| Approach: | They propose a plug-and-play framework for video-guided machine translation with multimodal large language models. |
| Outcome: | The proposed framework improves performance of MLLMs while reducing computational cost. |
Your Language Model May Think Too Rigidly: Achieving Reasoning Consistency with Symmetry-Enhanced Training (2025.findings-acl)
Copied to clipboard
Yihang Yao, Zhepeng Cen, Miao Li, William Han, Yuyou Zhang, Emerson Liu, Zuxin Liu, Chuang Gan, Ding Zhao
| Challenge: | Large Language Models (LLMs) have demonstrated strong reasoning capabilities across various tasks. |
| Approach: | They propose a data-centric approach that enhances LLMs’ awareness of symmetry in query variations and propose syMmetry-ENhanceD (MEND) data augmentation. |
| Outcome: | Extensive experiments on logical and arithmetic reasoning tasks show that the proposed approach improves model robustness at the knowledge extraction stage through query augmentation. |
HAT: Hardware-Aware Transformers for Efficient Natural Language Processing (2020.acl-main)
Copied to clipboard
| Challenge: | Extensive experiments on four machine translation tasks demonstrate that HAT can discover efficient models for different hardware (CPU, GPU, IoT device). |
| Approach: | They propose to construct a large design space with arbitrary encoder-decoder attention and heterogeneous layers and then train a SuperTransformer that efficiently produces many SubTransformers with weight sharing. |
| Outcome: | The proposed framework can find efficient models for different hardware (CPU, GPU, IoT device) it achieves 3 speedup, 3.7 smaller size over baseline Transformer; 2.7 speed up, 3.6 smaller sizes over Evolved Transformer with 12,041 less search cost and no performance loss. |
Beyond Sentence-level Labels: Integrating Conversational Context and Personal Experience for Natural Emotional Expression (2026.findings-acl)
Copied to clipboard
Haiyang Sun, Chenyang Le, Wei Wang, Leying Zhang, Chuang Li, Bing Han, Chenda Li, Mengxiao Bi, Yanmin Qian
| Challenge: | Existing systems rely on sentence-level labels, which fails to capture the subtle nuances of human affect. |
| Approach: | They propose to use a large-scale, context-aware speech corpus derived from multi-speaker audiobooks to generate a speech that is human-like. |
| Outcome: | The proposed model outperforms existing methods in terms of emotional expression accuracy and naturalness. |