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arXiv · 2503.02251

Tailoring Table Retrieval from a Field-aware Hybrid Matching Perspective

Da Li·Keping Bi·Jiafeng Guo·Xueqi Cheng
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Abstract

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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Da Li, Keping Bi, Jiafeng Guo, Xueqi Cheng. 2025-03-04. Tailoring Table Retrieval from a Field-aware Hybrid Matching Perspective. https://arxiv.org/abs/2503.02251

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