Papers by Yanru Zhou
Separation and Fusion: A Novel Multiple Token Linking Model for Event Argument Extraction (2024.naacl-long)
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Jing Xu, Dandan Song, Siu Hui, Zhijing Wu, Meihuizi Jia, Hao Wang, Yanru Zhou, Changzhi Zhou, Ziyi Yang
| Challenge: | Existing methods for event argument extraction (EAE) lack cross-event information and require longer role sequences . et al. (2017): outperforms state-of-the-art methods for EE. |
| Approach: | They propose a separation-and-fusion paradigm to separate the acquisition of cross-event information and fuse it into the argument extraction of a target event. |
| Outcome: | The proposed model outperforms the state-of-the-art models on four widely used datasets. |
Dynamically Fused Graph Network for Multi-hop Reasoning (P19-1)
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| Challenge: | Text-based question answering (TBQA) has been studied extensively in recent years. |
| Approach: | They propose a Dynamically Fused Graph Network to answer questions requiring multiple scattered evidence and reasoning over them. |
| Outcome: | The proposed method achieves competitive results on a public TBQA dataset and produces interpretable reasoning chains. |
DiTReducio: A Training-Free Acceleration for DiT-Based TTS via Progressive Calibration (2026.findings-acl)
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| Challenge: | Existing training-free acceleration approaches for text-to-speech models are constrained by training costs. |
| Approach: | They propose a training-free acceleration framework that compresses computations in DiT-based TTS models . they propose Temporal Skipping and Branch Skipping to eliminate redundant computations . |
| Outcome: | Experimental results show that the proposed framework reduces FLOPs and improves RTF by 37.1%. |
The Impact of Large Language Models in Academia: from Writing to Speaking (2025.findings-acl)
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| Challenge: | Large language models (LLMs) are impacting human society, especially in textual information. |
| Approach: | They propose to build an automated monitoring platform to track the impact of large language models on human expression. |
| Outcome: | The results show that LLM-style words such as significant are used more frequently in abstracts and oral presentations. |
PEK: A Parameter-Efficient Framework for Knowledge-Grounded Dialogue Generation (2024.findings-acl)
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| Challenge: | Pre-trained language models have shown great dialogue generation capability in different scenarios, but the huge VRAM consumption when fine-tuning them is one of their drawbacks. |
| Approach: | They propose a parameter-efficient framework for knowledge-enhanced dialogue generation that leverages external knowledge documents and knowledge graphs to enhance its generation capabilities. |
| Outcome: | The proposed framework outperforms baseline methods on multiple evaluation metrics on Wizard of Wikipedia and CMU_DoG datasets. |