Recently, the structural reading comprehension (SRC) task on web pages has attracted increasing research interests. Although previous SRC work has leveraged extra information such as HTML tags or XPaths, the informative topology of web pages is not effectively exploited. In this work, we propose a Topological Information Enhanced model (TIE), which transforms the token-level task into a tag-level task by introducing a two-stage process (i.e. node locating and answer refining). Based on that, TIE integrates Graph Attention Network (GAT) and Pre-trained Language Model (PLM) to leverage the topological information of both logical structures and spatial structures. Experimental results demonstrate that our model outperforms strong baselines and achieves state-of-the-art performances on the web-based SRC benchmark WebSRC at the time of writing. The code of TIE will be publicly available at https://github.com/X-LANCE/TIE.

本文提出了一种基于网络页面拓扑信息的阅读理解模型TIE，通过将令牌级任务转化为标签级任务，引入了两个步骤（即节点定位和答案细化），并整合了图形注意力网络和预训练语言模型，实验结果表明其性能优于强基线并达到了最新水平。

TIE: 基于拓扑信息增强的网络页面结构阅读理解