Exploring the generalizability of a text-to-SQL parser is essential for a system to automatically adapt the real-world databases. Previous investigation works mostly focus on lexical diversity, including the influence of the synonym and perturbations in both natural language questions and databases. However, the structural variability of database schema (DS), as a widely seen real-world scenario, is yet underexplored. Specifically, confronted with the same input question, the target SQL may be represented in different ways when the DS comes to a different structure. In this work, we provide in-depth discussions about the schema generalizability challenge of text-to-SQL tasks. We observe that current datasets are too templated to study schema generalization. To collect suitable test data, we propose a framework to generate novel text-to-SQL data via automatic and synchronous (DS, SQL) pair altering. When evaluating state-of-the-art text-to-SQL models on the synthetic samples, performance is significantly degraded, which demonstrates the limitation of current research regarding schema generalization.

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.