为复杂表格设计新型嵌入，精准捕捉横纵层次与嵌套结构。
Tabular Embeddings for Tables with Bi-Dimensional Hierarchical Metadata and Nesting
- 定义双向表格坐标，分离横纵元数据与数据上下文。
- 在5个大规模数据集上，MAP指标最高提升0.28，优于现有模型。
- 适合处理含复杂层级与嵌套的结构化表格任务。
嵌入向量作为现实世界实体的紧凑表示，在自然语言处理、计算机视觉和数据管理中广泛应用。本文提出一种专用于编码复杂二维表格上下文的新嵌入方法，支持横向与纵向的层次化元数据及嵌套结构。通过引入新的可见性矩阵，分离水平、垂直元数据与数据上下文，并针对此类复杂结构化数据优化嵌入表示。在5个大规模结构化数据集和3项主流下游任务上的评估显示，本方法显著优于现有最先进模型，最大MAP提升达0.28。尽管GPT-4 LLM+RAG在MRR指标上略优（最高提升0.1），但本方法在MAP上反超最多达0.42。
原文摘要 · Abstract (English)
Embeddings serve as condensed vector representations for real-world entities, finding applications in Natural Language Processing (NLP), Computer Vision, and Data Management across diverse downstream tasks. Here, we introduce novel specialized embeddings optimized, and explicitly tailored to encode the intricacies of complex 2-D context in tables, featuring horizontal, vertical hierarchical metadata, and nesting. To accomplish that we define the Bi-dimensional tabular coordinates, separate horizontal, vertical metadata and data contexts by introducing a new visibility matrix, encode units and nesting through the embeddings specifically optimized for mimicking intricacies of such complex structured data. Through evaluation on 5 large-scale structured datasets and 3 popular downstream tasks, we observed that our solution outperforms the state-of-the-art models with the significant MAP delta of up to 0.28. GPT-4 LLM+RAG slightly outperforms us with MRR delta of up to 0.1, while we outperform it with the MAP delta of up to 0.42.
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