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In the real-world question answering scenarios, hybrid form combining bothtabular and textual contents has attracted more and more attention, among whichnumerical reasoning problem is one of the most typical and challengingproblems. Existing methods usually adopt encoder-decoder framework to representhybrid contents and generate answers. However, it can not capture the richrelationship among numerical value, table schema, and text information on theencoder side. The decoder uses a simple predefined operator classifier which isnot flexible enough to handle numerical reasoning processes with diverseexpressions. 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-texthybrid contents as an expression tree generation task. Moreover, we propose anovel relational graph modeling method, which models alignment betweenquestions, tables, and paragraphs. We validated our model on the publiclyavailable table-text hybrid QA benchmark (TAT-QA). The proposed RegHNTsignificantly outperform the baseline model and achieve state-of-the-artresults. We openly released the source code and data athttps://github.com/lfy79001/RegHNT (2022-05-05).

Answering Numerical Reasoning Questions in Table-Text Hybrid Contents with Graph-based Encoder and Tree-based Decoder