论文标题
用基于图的编码器和基于树的解码器回答表Text混合内容中的数值推理问题
Answering Numerical Reasoning Questions in Table-Text Hybrid Contents with Graph-based Encoder and Tree-based Decoder
论文作者
论文摘要
在现实世界中的回答场景中，结合表格和文本内容的混合形式吸引了越来越多的关注，其中数值推理问题是最典型和最具挑战性的问题之一。现有方法通常采用编码器框架来表示混合内容并生成答案。但是，它无法捕获编码器侧数值，表格架构和文本信息之间的丰富关系。解码器使用一个简单的预定义运算符分类器，该分类器的灵活性不足以处理具有不同表达式的数值推理过程。为了解决这些问题，本文提出了一个\ textbf {re} lational \ textbf {g} raph增强\ textbf {h} ybrid table-text \ textbf {n}用\ textbf {t textbf {t} ree decoder（retexter decoder）（\ text decoder（\ textbff {regnt}））。它将对表text混合内容的回答的数值问题建模为表达树生成任务。此外，我们提出了一种新颖的关系图建模方法，该方法模拟了问题，表和段落之间的对齐方式。我们验证了公开可用的Table-Text Hybrid QA基准（TAT-QA）的模型。拟议的Reghnt显着超过了基线模型，并实现了最先进的结果。我们在https://github.com/lfy79001/reghnt（2022-05-05）公开发布了源代码和数据。
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).
