Papers by Yanqing Guo

2 papers
Multi-Attribute Controlled Text Generation with Contrastive-Generator and External-Discriminator (2022.coling-1)

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Challenge: Existing studies on controlled text generation focus on single-attribute control, but in practical applications, they lack controllability.
Approach: They propose a framework for multi-attribute controlled text generation that can effectively generate texts with more attributes.
Outcome: The proposed framework achieves remarkable controllability while keeping the text fluent and diverse.
Prefix-diffusion: A Lightweight Diffusion Model for Diverse Image Captioning (2024.lrec-main)

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Challenge: Existing image captioning models require large trainable parameters to bridge visual and textual representations.
Approach: They propose a lightweight image captioning network in combination with continuous diffusion that injects prefix image embeddings into denoising process of diffusion model.
Outcome: The proposed method generates diverse captions with relatively less parameters while maintaining fluency and relevance compared with other models.

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