Papers by Ruize Wang
Nearest Neighbor Machine Translation is Meta-Optimizer on Output Projection Layer (2023.emnlp-main)
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| Challenge: | Nearest Neighbor Machine Translation (kNN-MT) is a powerful domain adaptation tool . the reasons for its success have not been thoroughly investigated . |
| Approach: | They propose to integrate pre-trained Neural Machine Translation models with token-level retrieval . they propose to implicitly execute gradient descent on the output projection layer of NMT . |
| Outcome: | The proposed approach outperforms model fine-tuning on in-domain tests while achieving better performance on out-of-domain sets. |
ComCLIP: Training-Free Compositional Image and Text Matching (2024.naacl-long)
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| Challenge: | erroneous semantics of individual entities are essentially confounders that cause the matching failure. |
| Approach: | They propose a training-free compositional CLIP model which disentangles input images into subjects, objects, and action subimages and composes CLIP’s vision encoder and text encoder to perform evolving matching over compositional text embedding and subimage embeddments. |
| Outcome: | The proposed model mitigates spurious correlations introduced by the pretrained CLIP models and dynamically evaluates the importance of each component. |
LitVISTA: A Benchmark for Narrative Orchestration in Literary Text (2026.acl-long)
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Mingzhe Lu, Yiwen Wang, Yanbing Liu, Qi You, Chong Liu, Ruize Qin, Haoyu Dong, Wenyu Zhang, JiaRui Zhang, Yue Hu, Yunpeng Li
| Challenge: | Existing large language models focus on causal coherence, neglecting the complex story arcs and orchestration inherent in human narratives. |
| Approach: | They propose a high-dimensional framework for narrative orchestration that unifies human and model perspectives while jointly characterizing narrative function and structure in a common space. |
| Outcome: | The proposed framework unifies human and model perspectives while jointly characterizing narrative function and structure in a common space. |
K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters (2021.findings-acl)
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Ruize Wang, Duyu Tang, Nan Duan, Zhongyu Wei, Xuanjing Huang, Jianshu Ji, Guihong Cao, Daxin Jiang, Ming Zhou
| Challenge: | Existing methods for injecting knowledge into pre-trained models are inconsistent and can flush out knowledge when multiple kinds of knowledge are injected. |
| Approach: | They propose a framework that retains the original parameters of pre-trained models fixed and supports the development of versatile knowledge-infused models. |
| Outcome: | The proposed framework retains the original parameters of the pre-trained model fixed and supports the development of versatile knowledge-infused models. |
Keep it Consistent: Topic-Aware Storytelling from an Image Stream via Iterative Multi-agent Communication (2020.coling-main)
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| Challenge: | Existing methods for visual storytelling construct text description independently for each image and roughly concatenate them as a story, which leads to the problem of generating semantically incoherent content. |
| Approach: | They propose a topic description task to detect the global semantic context of an image stream and a story is then constructed with the guidance of the topic description. |
| Outcome: | The proposed framework can generate stories with higher quality compared to state-of-the-art methods on a VIST dataset. |
Neural Deepfake Detection with Factual Structure of Text (2020.emnlp-main)
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| Challenge: | Existing approaches to deepfake detection typically represent documents with coarse-grained representations, but they struggle to capture factual structures of documents. |
| Approach: | They propose a graph-based model that captures factual structures of documents for deepfake detection. |
| Outcome: | The proposed model improves strong base models built with RoBERTa on two public deepfake datasets. |
Enhancing Machine Translation with Self-Supervised Preference Data (2025.acl-long)
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| Challenge: | Current approaches to constructing preference data rely on human annotations. |
| Approach: | They propose a framework which efficiently constructs translation preference data for iterative training. |
| Outcome: | The proposed framework improves translation preference data on large language models. |
Leveraging Declarative Knowledge in Text and First-Order Logic for Fine-Grained Propaganda Detection (2020.emnlp-main)
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| Challenge: | Existing methods for fine-grained propaganda detection are not based on input-output data, but instead use declarative knowledge to detect propagandistic text fragments. |
| Approach: | They propose a method to inject declarative knowledge of fine-grained propaganda techniques into training data to get better representations of propagandistic texts. |
| Outcome: | The proposed method achieves superior performance on a large dataset for propaganda detection. |