Papers by Guojun Wu
ICU: Conquering Language Barriers in Vision-and-Language Modeling by Dividing the Tasks into Image Captioning and Language Understanding (2023.findings-emnlp)
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| Challenge: | Existing models that use multilingual captions for images have limited results due to the scarcity of training data. |
| Approach: | They propose a multilingual vision-and-language model that divides a V&L task into two stages . they propose IC, which takes the caption as the alt text and performs cross-lingual language understanding . |
| Outcome: | The proposed model can achieve state-of-the-art results for five languages and comparable results for the rest. |
Evaluating Automatic Metrics with Incremental Machine Translation Systems (2024.findings-emnlp)
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| Challenge: | Existing studies have shown that neural metrics are more reliable than non-neural metrics. |
| Approach: | They propose to use commercial machine translations to evaluate machine translation metrics based on their preference for more recent outputs. |
| Outcome: | The proposed dataset confirms several previous findings, including the advantage of neural metrics over non-neural ones, and also explores the debated issue of how MT quality affects metric reliability. |
PFDial: A Structured Dialogue Instruction Fine-tuning Method Based on UML Flowcharts (2025.findings-acl)
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Ming Zhang, Yuhui Wang, Yujiong Shen, Tingyi Yang, Changhao Jiang, Yilong Wu, Shihan Dou, Qinhao Chen, Zhiheng Xi, Zhihao Zhang, Yi Dong, Zhen Wang, Zhihui Fei, Mingyang Wan, Tao Liang, Guojun Ma, Qi Zhang, Tao Gui, Xuanjing Huang
| Challenge: | Large Language Models (LLMs) have shown remarkable progress in dialogue and reasoning, but they struggle to solve strictly constrained dialogue tasks. |
| Approach: | They construct a dataset that contains 12,705 high-quality Chinese dialogue instructions from 440 flowcharts containing 5,055 process nodes. |
| Outcome: | The proposed model outperforms GPT-4o models on backward transitions and outperformed GPT-42 models on the same dataset. |