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

• URL: http://arxiv.org/abs/2209.07692v1
• Date: Fri, 16 Sep 2022 03:15:12 GMT
• Title: Answering Numerical Reasoning Questions in Table-Text Hybrid Contents with Graph-based Encoder and Tree-based Decoder
• Authors: Fangyu Lei, Shizhu He, Xiang Li, Jun Zhao, Kang Liu
• Abstract summary: This paper proposes a textbfRelational textbfGraph enhanced textbfHybrid table-text textbfNumerical reasoning model with textbfRegHNT. It models the numerical question answering over table-text hybrid contents as an expression tree generation task. We validated our model on the publicly available table-text hybrid QA benchmark (TAT-QA)
• Score: 19.429216786198577
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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\footnote{We openly released the source code and data at~\url{https://github.com/lfy79001/RegHNT}}~(2022-05-05).

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