Ruize Wang


2021

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K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters
Ruize Wang | Duyu Tang | Nan Duan | Zhongyu Wei | Xuanjing Huang | Jianshu Ji | Guihong Cao | Daxin Jiang | Ming Zhou
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

2020

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Keep it Consistent: Topic-Aware Storytelling from an Image Stream via Iterative Multi-agent Communication
Ruize Wang | Zhongyu Wei | Ying Cheng | Piji Li | Haijun Shan | Ji Zhang | Qi Zhang | Xuanjing Huang
Proceedings of the 28th International Conference on Computational Linguistics

Visual storytelling aims to generate a narrative paragraph from a sequence of images automatically. Existing approaches construct text description independently for each image and roughly concatenate them as a story, which leads to the problem of generating semantically incoherent content. In this paper, we propose a new way for visual storytelling by introducing a topic description task to detect the global semantic context of an image stream. A story is then constructed with the guidance of the topic description. In order to combine the two generation tasks, we propose a multi-agent communication framework that regards the topic description generator and the story generator as two agents and learn them simultaneously via iterative updating mechanism. We validate our approach on VIST dataset, where quantitative results, ablations, and human evaluation demonstrate our method’s good ability in generating stories with higher quality compared to state-of-the-art methods.

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Neural Deepfake Detection with Factual Structure of Text
Wanjun Zhong | Duyu Tang | Zenan Xu | Ruize Wang | Nan Duan | Ming Zhou | Jiahai Wang | Jian Yin
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Deepfake detection, the task of automatically discriminating machine-generated text, is increasingly critical with recent advances in natural language generative models. Existing approaches to deepfake detection typically represent documents with coarse-grained representations. However, they struggle to capture factual structures of documents, which is a discriminative factor between machine-generated and human-written text according to our statistical analysis. To address this, we propose a graph-based model that utilizes the factual structure of a document for deepfake detection of text. Our approach represents the factual structure of a given document as an entity graph, which is further utilized to learn sentence representations with a graph neural network. Sentence representations are then composed to a document representation for making predictions, where consistent relations between neighboring sentences are sequentially modeled. Results of experiments on two public deepfake datasets show that our approach significantly improves strong base models built with RoBERTa. Model analysis further indicates that our model can distinguish the difference in the factual structure between machine-generated text and human-written text.

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Leveraging Declarative Knowledge in Text and First-Order Logic for Fine-Grained Propaganda Detection
Ruize Wang | Duyu Tang | Nan Duan | Wanjun Zhong | Zhongyu Wei | Xuanjing Huang | Daxin Jiang | Ming Zhou
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

We study the detection of propagandistic text fragments in news articles. Instead of merely learning from input-output datapoints in training data, we introduce an approach to inject declarative knowledge of fine-grained propaganda techniques. Specifically, we leverage the declarative knowledge expressed in both first-order logic and natural language. The former refers to the logical consistency between coarse- and fine-grained predictions, which is used to regularize the training process with propositional Boolean expressions. The latter refers to the literal definition of each propaganda technique, which is utilized to get class representations for regularizing the model parameters. We conduct experiments on Propaganda Techniques Corpus, a large manually annotated dataset for fine-grained propaganda detection. Experiments show that our method achieves superior performance, demonstrating that leveraging declarative knowledge can help the model to make more accurate predictions.