Papers by Yue Che
Dynamic Fusion Network for Multi-Domain End-to-end Task-Oriented Dialog (2020.acl-main)
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| Challenge: | Recent studies show remarkable success in end-to-end task-oriented dialog systems . however, most models rely on large training data, which is difficult to scalable for new domains with limited labeled data. |
| Approach: | They propose a shared-private network which exploits the relevance between the target domain and each domain. |
| Outcome: | The proposed model outperforms existing methods on multi-domain dialogue by 13.9% on average. |
End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directions (2023.emnlp-main)
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| Challenge: | End-to-end task-oriented dialogue (EToD) can generate responses in an end-to end fashion without modular training, which attracts escalating popularity. |
| Approach: | They present a systematic review of EToD and propose a unified perspective to summarize existing approaches and recent trends. |
| Outcome: | The proposed approaches can generate responses in an end-to-end fashion without modular training, which attracts escalating popularity. |
Mitigating Hallucinations in Vision-Language Models through Image-Guided Head Suppression (2025.emnlp-main)
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| Challenge: | Existing methods for reducing hallucinations incur a significant increase in latency. |
| Approach: | They propose a task-agnostic attention-guided head suppression strategy that can be seamlessly integrated during inference without incurring significant compute or latency overhead. |
| Outcome: | The proposed approach reduces hallucinations by 2.7x while maintaining F1 and improves throughput by 1.8% compared to existing methods. |