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Open-domain table question answering aims to provide answers to a question byretrieving and extracting information from a large collection of tables.Existing studies of open-domain table QA either directly adopt text retrievalmethods or consider the table structure only in the encoding layer for tableretrieval, which may cause syntactical and structural information loss duringtable scoring. To address this issue, we propose a syntax- and structure-awareretrieval method for the open-domain table QA task. It provides syntacticalrepresentations for the question and uses the structural header and valuerepresentations for the tables to avoid the loss of fine-grained syntacticaland structural information. Then, a syntactical-to-structural aggregator isused to obtain the matching score between the question and a candidate table bymimicking the human retrieval process. Experimental results show that ourmethod achieves the state-of-the-art on the NQ-tables dataset and overwhelmsstrong baselines on a newly curated open-domain Text-to-SQL dataset.

Enhancing Open-Domain Table Question Answering via Syntax- and Structure-aware Dense Retrieval