The task of natural language table retrieval (NLTR) seeks to retrieve
semantically relevant tables based on natural language queries. Existing
learning systems for this task often treat tables as plain text based on the
assumption that tables are structured as dataframes. However, tables can have
complex layouts which indicate diverse dependencies between subtable
structures, such as nested headers. As a result, queries may refer to different
spans of relevant content that is distributed across these structures.
Moreover, such systems fail to generalize to novel scenarios beyond those seen
in the training set. Prior methods are still distant from a generalizable
solution to the NLTR problem, as they fall short in handling complex table
layouts or queries over multiple granularities. To address these issues, we
propose Graph-based Table Retrieval (GTR), a generalizable NLTR framework with
multi-granular graph representation learning. In our framework, a table is
first converted into a tabular graph, with cell nodes, row nodes and column
nodes to capture content at different granularities. Then the tabular graph is
input to a Graph Transformer model that can capture both table cell content and
the layout structures. To enhance the robustness and generalizability of the
model, we further incorporate a self-supervised pre-training task based on
graph-context matching. Experimental results on two benchmarks show that our
method leads to significant improvements over the current state-of-the-art
systems. Further experiments demonstrate promising performance of our method on
cross-dataset generalization, and enhanced capability of handling complex
tables and fulfilling diverse query intents. Code and data are available at
this https URL

提出了一种基于图的表格检索框架，该框架可以通过学习多颗粒度图表表示来处理复杂的布局，包括表头嵌套，进而实现自然语言表检索问题的泛化。