Papers by Xinpeng Zhou
CHAE: Fine-Grained Controllable Story Generation with Characters, Actions and Emotions (2022.coling-1)
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| Challenge: | Existing studies on story generation focus on coarse-grained control of the story, neglecting the details of the narrative. |
| Approach: | They propose a model for fine-grained control on the story that allows the generation of customized stories with characters, corresponding actions and emotions arbitrarily assigned. |
| Outcome: | The proposed method has strong controllability to generate customized stories according to the fine-grained personalized guidance. |
ToViLaG: Your Visual-Language Generative Model is Also An Evildoer (2023.emnlp-main)
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| Challenge: | Recent large-scale Visual-Language Generative Models (VLGMs) generate toxic content, e.g., offensive text and pornography images, raising significant ethical risks. |
| Approach: | They propose a bottleneck-based detoxification method to reduce toxicity while maintaining comparable generation quality. |
| Outcome: | The proposed method could reduce toxicity while maintaining comparable generation quality. |
Event Detection without Triggers (N19-1)
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| Challenge: | Existing approaches to event detection require annotated triggers and event types in training data. |
| Approach: | They propose a framework that encodes the representation of a sentence based on target event types. |
| Outcome: | The proposed framework achieves competitive performances compared with state-of-the-art methods. |