针对表格不同字段设计混合匹配策略，提升表格检索效果
Tailoring Table Retrieval from a Field-aware Hybrid Matching Perspective
- 按字段区分匹配粒度，细胞用词级匹配，标题用句级匹配
- 在NQ-TABLES和OTT-QA上超越现有最佳方法
- 适合需要精准表格信息检索的研究者与工程师
表格检索对于通过结构化数据获取信息至关重要，但相较于文本检索研究较少。表格的行/列结构及各字段（包括标题、表头、单元格）具有独特性，不同字段存在差异化的匹配偏好：单元格因碎片化和细节丰富，更适合词/短语级细粒度匹配，而标题则更适配句子/段落级粗粒度匹配。因此，需设计面向表格的检索器以满足各字段的匹配需求。为此，我们提出一种场域感知的混合匹配检索器——THYME（Table-tailored HYbrid Matching rEtriever），从场域感知的混合匹配视角解决表格检索问题。在NQ-TABLES和OTT-QA两个基准测试上的实证结果表明，THYME显著优于现有最先进基线模型。全面分析验证了不同字段间的匹配偏好差异，并支持了THYME的设计合理性。
原文摘要 · Abstract (English)
Table retrieval, essential for accessing information through tabular data, is less explored compared to text retrieval. The row/column structure and distinct fields of tables (including titles, headers, and cells) present unique challenges. For example, different table fields have varying matching preferences: cells may favor finer-grained (word/phrase level) matching over broader (sentence/passage level) matching due to their fragmented and detailed nature, unlike titles. This necessitates a table-specific retriever to accommodate the various matching needs of each table field. Therefore, we introduce a Table-tailored HYbrid Matching rEtriever (THYME), which approaches table retrieval from a field-aware hybrid matching perspective. Empirical results on two table retrieval benchmarks, NQ-TABLES and OTT-QA, show that THYME significantly outperforms state-of-the-art baselines. Comprehensive analyses confirm the differing matching preferences across table fields and validate the design of THYME.
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