  Tabular reasoning involves interpreting unstructured queries against
structured tables, requiring a synthesis of textual understanding and symbolic
reasoning. Existing methods rely on either of the approaches and are
constrained by their respective limitations. Textual reasoning excels in
semantic interpretation unlike symbolic reasoning (SQL logic), but falls short
in mathematical reasoning where SQL excels. In this paper, we introduce a novel
algorithm H-STAR, comprising table extraction and adaptive reasoning,
integrating both symbolic and semantic (text-based) approaches. To enhance
evidence extraction, H-STAR employs a multi-view approach, incorporating
step-by-step row and column retrieval. It also adapts reasoning strategies
based on question types, utilizing symbolic reasoning for quantitative and
logical tasks, and semantic reasoning for direct lookup and complex lexical
queries. Our extensive experiments demonstrate that H-STAR significantly
outperforms state-of-the-art methods across three tabular question-answering
(QA) and fact-verification datasets, underscoring its effectiveness and
efficiency.
