  In the real-world question answering scenarios, hybrid form combining both
tabular and textual contents has attracted more and more attention, among which
numerical reasoning problem is one of the most typical and challenging
problems. Existing methods usually adopt encoder-decoder framework to represent
hybrid contents and generate answers. However, it can not capture the rich
relationship among numerical value, table schema, and text information on the
encoder side. The decoder uses a simple predefined operator classifier which is
not flexible enough to handle numerical reasoning processes with diverse
expressions. To address these problems, this paper proposes a
\textbf{Re}lational \textbf{G}raph enhanced \textbf{H}ybrid table-text
\textbf{N}umerical reasoning model with \textbf{T}ree decoder
(\textbf{RegHNT}). It models the numerical question answering over table-text
hybrid contents as an expression tree generation task. Moreover, we propose a
novel relational graph modeling method, which models alignment between
questions, tables, and paragraphs. We validated our model on the publicly
available table-text hybrid QA benchmark (TAT-QA). The proposed RegHNT
significantly outperform the baseline model and achieve state-of-the-art
results. We openly released the source code and data at
https://github.com/lfy79001/RegHNT (2022-05-05).
