  Open-domain table question answering aims to provide answers to a question by
retrieving and extracting information from a large collection of tables.
Existing studies of open-domain table QA either directly adopt text retrieval
methods or consider the table structure only in the encoding layer for table
retrieval, which may cause syntactical and structural information loss during
table scoring. To address this issue, we propose a syntax- and structure-aware
retrieval method for the open-domain table QA task. It provides syntactical
representations for the question and uses the structural header and value
representations for the tables to avoid the loss of fine-grained syntactical
and structural information. Then, a syntactical-to-structural aggregator is
used to obtain the matching score between the question and a candidate table by
mimicking the human retrieval process. Experimental results show that our
method achieves the state-of-the-art on the NQ-tables dataset and overwhelms
strong baselines on a newly curated open-domain Text-to-SQL dataset.
