Papers by Zonghan Yang
Reducing Word Omission Errors in Neural Machine Translation: A Contrastive Learning Approach (P19-1)
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| Challenge: | Existing methods for reducing word omission errors in neural machine translation are prone to omit essential words on the source side. |
| Approach: | They propose a contrastive learning approach to reduce word omission errors in NMT by omitting words. |
| Outcome: | The proposed approach achieves better translation performance than baseline methods on Chinese-to-English, German-to English, and Russian-toEnglish translation tasks. |
Scaffolding Coordinates to Promote Vision-Language Coordination in Large Multi-Modal Models (2025.coling-main)
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| Challenge: | Existing prompting techniques for Large Multi-Modal Models (LMMs) focus on improving textual reasoning or leveraging tools for image preprocessing, lacking a simple and general visual prompting scheme to promote vision-language coordination. |
| Approach: | They propose a prompting scheme that scaffolds coordinates to promote vision-language coordination in Large Multi-Modal Models (LMMs) they overlay a dot matrix within the image as visual information anchors and leverage multi-dimensional coordinates as textual positional references. |
| Outcome: | Experiments on a wide range of vision-language tasks show the superiority of SCAFFOLD prompting over the textual Chain-of-Thought prompting. |
Exploring the Impact of Model Scaling on Parameter-Efficient Tuning (2023.emnlp-main)
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Yusheng Su, Chi-Min Chan, Jiali Cheng, Yujia Qin, Yankai Lin, Shengding Hu, Zonghan Yang, Ning Ding, Xingzhi Sun, Guotong Xie, Zhiyuan Liu, Maosong Sun
| Challenge: | Parameter-efficient tuning (PET) methods can drive large pre-trained language models by training only minimal parameters. |
| Approach: | They propose a parameter-efficient tuning method that is compatible with a tunable module and uses a random number generator to optimize fewer table parameters. |
| Outcome: | The proposed method is compatible with a tunable module and tested on 11 NLP tasks. |
Alternated Training with Synthetic and Authentic Data for Neural Machine Translation (2021.findings-acl)
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| Challenge: | Existing approaches to synthesizing data in NMT focus on leveraging monolingual data in training. |
| Approach: | They propose alternated training with synthetic and authentic data to improve NMT models' performance. |
| Outcome: | The proposed approach improves Chinese-English and German-English translation tasks over strong baselines. |
Pi-SQL: Enhancing Text-to-SQL with Fine-Grained Guidance from Pivot Programming Languages (2025.findings-emnlp)
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| Challenge: | Existing prompt-based methods craft meticulous text guidelines and examples to facilitate SQL generation, but their accuracy is hindered by the large semantic gap between the texts and the low-resource SQL programs. |
| Approach: | They propose to use Python as a pivot to bridge between natural language query and SQL program. |
| Outcome: | The proposed method improves the execution accuracy of the best-performing baseline by up to 3.20. |
Bridging the Gap between Decision and Logits in Decision-based Knowledge Distillation for Pre-trained Language Models (2023.acl-long)
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| Challenge: | Existing knowledge distillation methods require access to internal information of teachers . however, such information is not always accessible for large pre-trained language models . |
| Approach: | They propose a method to estimate logits from the decision distributions using logits theoretically and empirically. |
| Outcome: | The proposed method outperforms baselines on natural language understanding and machine reading comprehension datasets. |
PANDA: Preference Adaptation for Enhancing Domain-Specific Abilities of LLMs (2024.findings-acl)
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| Challenge: | Large language models have demonstrated considerable capabilities across various tasks . however, they often fall short of the performance achieved by domain-specific state-of-the-art models . |
| Approach: | They propose a tuning-free method to augment domain-specific abilities of Large language models . they leverage insights from the response preference of expert models to augment LLMs . |
| Outcome: | The proposed method outperforms the expert model on 4 ScienceWorld tasks. |