用分层Transformer建模食谱步骤内与步骤间的复杂关系
RecipeNet: A Hierarchical Transformer for Recipe Data
- 分层Transformer捕捉每步内部字段交互与跨步顺序依赖
- 在多个食谱数据集上超越传统表格模型表现
- 适合需要理解复杂流程的材料/制药领域研究者
食谱数据广泛存在于材料合成、药物配方和工业制造等领域，其流程以包含异构结构化字段的有序步骤序列形式呈现。现有表格学习方法通常将这种结构扁平化为固定模式表示，难以捕捉字段间的层级交互与流程依赖关系。我们提出RecipeNet，一种分层Transformer架构，通过堆叠的Transformer编码器分别建模每步内的字段级交互和跨步的序列依赖。在多个食谱数据集和任务上的实验表明，RecipeNet持续优于现有表格模型，凸显了分层与序列建模在食谱表征学习中的价值。
原文摘要 · Abstract (English)
Recipe data arises in domains such as materials synthesis, pharmaceutical formulation, and industrial manufacturing, where procedures are represented as ordered sequences of steps containing heterogeneous structured fields. Existing tabular learning methods typically flatten this structure into fixed-schema representations, limiting their ability to capture hierarchical field interactions and procedural dependencies. We propose RecipeNet, a hierarchical Transformer architecture that encodes field-level interactions within each step and sequential dependencies across steps through stacked Transformer encoders. Experiments on multiple recipe datasets and tasks demonstrate that RecipeNet consistently outperforms existing tabular models, highlighting the value of hierarchical and sequential modeling for recipe representation learning.
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