仅凭作者信息和研究构想，就能预判论文能否被接收。
Capability-Aware Early-Stage Research Idea Evaluation
- 用三路Transformer融合作者信息、能力推断与研究想法
- 在无完整论文情况下，预测准确率显著优于基线模型
- 适合科研资助评审与早期研究方向评估
在研究尚未投入大量资源前，预测其潜在成果，对优化科研资源配置具有重要意义。现有方法多依赖已完成的论文或同行评审，我们提出一种能力感知框架，仅通过作者信息和研究构想即可预测论文接受与否及评分，无需完整文本或实验结果。该方法采用三路Transformer架构，灵活融合作者信息、推断的能力表征与研究想法，并设计两阶段结构学习能力表示。实验表明，相比微调bert-base和bert-large的单路模型，本方法显著提升预测性能，能力预测有效增强了最终模型准确性。该方法可用于早期研究结果预测与科学资源配置。
原文摘要 · Abstract (English)
Predicting the outcomes of research ideas at their conceptual stage (i.e. before significant resources are committed) holds great potential for optimizing scientific resource allocation and research planning. While existing methods rely heavily on finished manuscripts or peer reviews, we propose a novel capability-aware framework that predicts paper acceptance and ratings using only author information and research ideas, without requiring full text or experimental results. Our approach integrates author information, (inferred) capability presentation, and research ideas through a three-way transformer architecture with flexible fusion mechanisms. We also introduce a two-stage architecture for learning the capability representation given the author information and idea. Experiments show that our method significantly outperform the single-way models by finetuning bert-base and bert-large, and the capability predicting significantly increase the predictive accuracy of the final model. The proposed method can be applied in both early-stage research outcome prediction and scientific resource allocation.
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