Papers by Chengyi Yang
Curriculum Pre-training for End-to-End Speech Translation (2020.acl-main)
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| Challenge: | End-to-end speech translation requires a powerful encoder to transcribe, understand and learn cross-lingual semantics simultaneously. |
| Approach: | They propose a curriculum pre-training method that includes an elementary course for transcription learning and two advanced courses for understanding the utterance and mapping words in two languages. |
| Outcome: | The proposed method improves on En-De and En-Fr speech translation benchmarks. |
Towards Better Graph-based Cross-document Relation Extraction via Non-bridge Entity Enhancement and Prediction Debiasing (2024.findings-acl)
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| Challenge: | Existing studies on relation extraction ignore non-bridge entities, leading to bias during inference. |
| Approach: | They propose a graph-based cross-document Relation Extraction model with non-bridge entity enhancement and prediction debiasing that integrates non-cross entities with target entities and bridge entities. |
| Outcome: | The proposed model outperforms baseline models on open and closed datasets. |
Mitigating Catastrophic Forgetting in Large Language Models with Self-Synthesized Rehearsal (2024.acl-long)
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Jianheng Huang, Leyang Cui, Ante Wang, Chengyi Yang, Xinting Liao, Linfeng Song, Junfeng Yao, Jinsong Su
| Challenge: | Existing methods to train LLMs on previous training data are not feasible in real-world applications because of catastrophic forgetting. |
| Approach: | They propose a framework that uses the LLM to generate synthetic instances for rehearsal and refine the instance outputs based on the synthetic inputs. |
| Outcome: | The proposed framework achieves superior or comparable performance compared to conventional rehearsal-based approaches while being more data-efficient. |
SCMAPR: Self-Correcting Multi-Agent Prompt Refinement for Complex-Scenario Text-to-Video Generation (2026.findings-acl)
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| Challenge: | Text-to-Video (T2V) generation is a challenge under complex scenarios. |
| Approach: | They propose a scenario-aware and self-correcting multi-agent prompt refinement framework for T2V prompting. |
| Outcome: | The proposed framework improves text-to-video alignment and overall generation quality under complex scenarios. |
Benchmarking Multi-National Value Alignment for Large Language Models (2025.findings-acl)
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Chengyi Ju, Weijie Shi, Chengzhong Liu, Jiaming Ji, Jipeng Zhang, Ruiyuan Zhang, Jiajie Xu, Yaodong Yang, Sirui Han, Yike Guo
| Challenge: | Existing studies on large language models focus on ethical reviews, failing to capture the diversity of national values. |
| Approach: | They propose a national value extraction pipeline to efficiently construct value assessment datasets and a model-based model with instruction tagging to process raw data sources. |
| Outcome: | The proposed benchmark evaluates the alignment of LLMs with the values of five major nations: China, the United States, the UK, France, and Germany. |
Locate-and-Focus: Enhancing Terminology Translation in Speech Language Models (2025.acl-long)
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Suhang Wu, Jialong Tang, Chengyi Yang, Pei Zhang, Baosong Yang, Junhui Li, Junfeng Yao, Min Zhang, Jinsong Su
| Challenge: | Existing methods for terminology translation struggle with interference from irrelevant noise. |
| Approach: | They propose a Locate-and-Focus method that locates terminologies within utterances to construct translation knowledge by minimizing irrelevant information for ST models. |
| Outcome: | The proposed method locates terminologies within utterances and enhances the success rate of terminology translation while maintaining robust general translation performance. |