Papers by Ruoyu Zhao
Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled Data (N19-1)
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| Challenge: | Neural machine translation systems have become state-of-the-art approaches for Grammatical Error Correction (GEC) task. |
| Approach: | They propose a copy-augmented architecture for the Grammatical Error Correction task by copying unchanged words from the source sentence to the target sentence. |
| Outcome: | The proposed architecture outperforms all recently published state-of-the-art results by a large margin. |
MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application (2026.acl-long)
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Xueqing Peng, Lingfei Qian, Yan Wang, Ruoyu Xiang, Yueru He, Yang Ren, Mingyang Jiang, Vincent Jim Zhang, Yuqing Guo, Jeff Zhao, Huan He, Yi Han, Yun Feng, Yuechen Jiang, Yupeng Cao, Haohang Li, Yangyang Yu, Xiaoyu Wang, Penglei Gao, Shengyuan Lin, Keyi Wang, Shanshan Yang, Yilun Zhao, Zhiwei Liu, Peng Lu, Jerry Huang, Suyuchen Wang, Triantafillos Papadopoulos, Polydoros Giannouris, Efstathia Soufleri, Nuo Chen, Zhiyang Deng, Heming Fu, Yijia Zhao, Mingquan Lin, Meikang Qiu, Kaleb E Smith, Arman Cohan, Xiao-Yang Liu, Jimin Huang, Guojun Xiong, Alejandro Lopez-Lira, Xi Chen, Junichi Tsujii, Jian-Yun Nie, Sophia Ananiadou, Qianqian Xie
| Challenge: | Existing evaluations of LLMs in finance are text-only, monolingual, and largely saturated by current models. |
| Approach: | They propose a multilingual and multimodal benchmark for evaluating LLMs in real financial contexts. |
| Outcome: | The first expert-annotated multilingual and multimodal benchmark is released . it evaluates 21 leading LLMs and shows they perform better in multilingual settings . |
LGA: LLM-GNN Aggregation for Temporal Evolution Attribute Graph Prediction (2025.emnlp-main)
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| Challenge: | Current methods focus on 1-hop neighborhood aggregation, lacking capability to capture complex structural interactions. |
| Approach: | They propose a framework that integrates structural information into attribute embeddings through an attribute embedded loss. |
| Outcome: | The proposed framework shows significant improvements over existing methods on real-world datasets. |
Denoising based Sequence-to-Sequence Pre-training for Text Generation (D19-1)
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| Challenge: | PoDA pre-trains encoders and decoders by denoising noise-corrupted text . Unlike encoder-only or decode-only methods, it can be used for text generation tasks without using any task-specific techniques. |
| Approach: | They propose a sequence-to-sequence (seq2sequ) pre-training method PoDA which denoises autoencoders by denoising noise-corrupted text. |
| Outcome: | The proposed method improves model performance over strong baselines without using any task-specific techniques and significantly speed up convergence. |
Hierarchical Multi-label Text Classification with Horizontal and Vertical Category Correlations (2021.emnlp-main)
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| Challenge: | Existing approaches to hierarchical multi-label text classification ignore vertical category correlations or exploit dependencies across levels without considering horizontal correlations . |
| Approach: | They propose a hierarchical multi-label text classification framework that considers both vertical and horizontal category correlations. |
| Outcome: | The proposed framework improves on real-world HMTC datasets with significant improvements over baselines. |
Multi-Perspective Context Aggregation for Semi-supervised Cloze-style Reading Comprehension (C18-1)
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| Challenge: | Recent studies have shown that cloze-style reading comprehension is a popular task for measuring the progress of natural language understanding. |
| Approach: | They propose a multi-perspective framework which can be seen as joint training of heterogeneous experts and aggregate context information from different perspectives. |
| Outcome: | The proposed framework achieves new state-of-the-art over previous strong baselines on a recently released cloze-test dataset. |