针对复杂查询设计细粒度表格检索方法，提升自然语言问答准确率。
Fine-Grained Table Retrieval Through the Lens of Complex Queries
- 将复杂查询分解为细粒度类型化子查询，增强语义理解
- 在复合查询与密集连接数据库上表现更稳健，召回率显著提升
- 适合需要精准表格检索的工业级问答系统
在自然语言下实现对表格和数据库的问答能力，已成为释放结构化数据价值的关键。这类系统首先需检索与自然语言查询相关的数据，已有多种方法被提出。本文提出一种表格检索机制DCTR，通过细粒度类型化查询分解与全局连通性感知，应对开放域问答中复杂使用场景带来的挑战。我们从查询复杂度与数据复杂度两个维度衡量检索复杂性，评估两种机制的有效性。在面向工业应用的基准测试中，DCTR展现出对高度复合查询和密集连接数据库的强鲁棒性。
原文摘要 · Abstract (English)
Enabling question answering over tables and databases in natural language has become a key capability in the democratization of insights from tabular data sources. These systems first require retrieval of data that is relevant to a given natural language query, for which several methods have been introduced. In this work we present and study a table retrieval mechanism devising fine-grained typed query decomposition and global connectivity-awareness (DCTR), to handle the challenges induced by open-domain question answering over relational databases in complex usage contexts. We evaluate the effectiveness of the two mechanisms through the lens of retrieval complexity which we measure along the axes of query- and data complexity. Our analyses over industry-aligned benchmarks illustrate the robustness of DCTR for highly composite queries and densely connected databases.
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