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Hybrid question answering (HybridQA) over the financial report contains bothtextual and tabular data, and requires the model to select the appropriateevidence for the numerical reasoning task. Existing methods based onencoder-decoder framework employ a expression tree-based decoder to solvenumerical reasoning problems. However, encoders rely more on Machine ReadingComprehension (MRC) methods, which take table serialization and text splicingas input, damaging the granularity relationship between table and text as wellas the spatial structure information of table itself. In order to solve theseproblems, the paper proposes a Multi-View Graph (MVG) Encoder to take therelations among the granularity into account and capture the relations frommultiple view. By utilizing MVGE as a module, we constuct Tabular View,Relation View and Numerical View which aim to retain the originalcharacteristics of the hybrid data. We validate our model on the publiclyavailable table-text hybrid QA benchmark (TAT-QA) and outperform thestate-of-the-art model.

Multi-View Graph Representation Learning for Answering Hybrid Numerical Reasoning Question