Multi-View Graph Representation Learning for Answering Hybrid Numerical
  Reasoning Question
Yifan Wei Fangyu Lei Yuanzhe Zhang Jun Zhao Kang Liu
Hybrid question answering (HybridQA) over the financial report contains both
textual and tabular data, and requires the model to select the appropriate
evidence for the numerical reasoning task. Existing methods based on
encoder-decoder framework employ a expression tree-based decoder to solve
numerical reasoning problems. However, encoders rely more on Machine Reading
Comprehension (MRC) methods, which take table serialization and text splicing
as input, damaging the granularity relationship between table and text as well
as the spatial structure information of table itself. In order to solve these
problems, the paper proposes a Multi-View Graph (MVG) Encoder to take the
relations among the granularity into account and capture the relations from
multiple view. By utilizing MVGE as a module, we constuct Tabular View,
Relation View and Numerical View which aim to retain the original
characteristics of the hybrid data. We validate our model on the publicly
available table-text hybrid QA benchmark (TAT-QA) and outperform the
state-of-the-art model.