Papers by Zhanglin Wu
Generative Annotation for ASR Named Entity Correction (2025.emnlp-main)
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Yuanchang Luo, Daimeng Wei, Shaojun Li, Hengchao Shang, Jiaxin Guo, Zongyao Li, Zhanglin Wu, Xiaoyu Chen, Zhiqiang Rao, Jinlong Yang, Hao Yang
| Challenge: | Existing named entity correction models fail to transcribe domain-speciffcnamed entities when theforms of the wrongly-transcribed words and the ground-truth entity are signiffcantly different. |
| Approach: | They propose a method that utilizes speech sound features to retrieve candidate entities . it uses speech sound feature to annotate entityerrors in ASR transcripts . |
| Outcome: | The proposed method can bring signiffcant improvement to entity accuracy. |
M-Ped: Multi-Prompt Ensemble Decoding for Large Language Models (2025.findings-emnlp)
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Jiaxin Guo, Daimeng Wei, Yuanchang Luo, Hengchao Shang, Zongyao Li, Jinlong Yang, Zhanglin Wu, Zhiqiang Rao, Shimin Tao, Hao Yang
| Challenge: | a new ensemble decoding approach enhances the performance of Large Language Models. |
| Approach: | They propose a multi-prompt ensemble decoding approach to enhance LLM performance . they submit n variations of prompts with X to LLMs in batch mode to decode and derive probability distributions . |
| Outcome: | The proposed method improves pass@k rates, LENS metrics and BLEU scores on diverse NLP tasks. |
Text Style Transfer Back-Translation (2023.acl-long)
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Daimeng Wei, Zhanglin Wu, Hengchao Shang, Zongyao Li, Minghan Wang, Jiaxin Guo, Xiaoyu Chen, Zhengzhe Yu, Hao Yang
| Challenge: | Current methods require large amount of bilingual training data, which is challenging and sometimes impossible task. |
| Approach: | They propose a method to modify the style of inputs by modifying the source side of BT data. |
| Outcome: | The proposed method significantly improves translation quality against popular BT benchmarks on high-resource and low-resourced language pairs. |
Combining the Best of Both Worlds: A Method for Hybrid NMT and LLM Translation (2025.findings-acl)
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Zhanglin Wu, Daimeng Wei, Xiaoyu Chen, Hengchao Shang, Jiaxin Guo, Zongyao Li, Yuanchang Luo, Jinlong Yang, Zhiqiang Rao, Hao Yang
| Challenge: | Large language models have advantages over neural machine translation systems, but they suffer from high computational costs and significant latency. |
| Approach: | They propose a scheduling policy that optimizes translation result while ensuring fast speed and as little LLM usage as possible. |
| Outcome: | The proposed model achieves optimal translation performance with less LLM usage on multilingual test sets. |