IM-TQA: A Chinese Table Question Answering Dataset with Implicit and Multi-type Table Structures

Anonymous

Keywords: table question answering, dataset, multi-type tables, implicit table structures

Abstract: Various datasets have been proposed to promote the development of Table Question Answering (TQA) technique. However, the problem setting of existing TQA benchmarks suffers from two limitations. First, they directly provide TQA models with explicit table structures where row headers and column headers of the table are explicitly annotated during inference. Second, they only consider tables of limited types and ignore other tables especially complex tables. Such simplified problem setting cannot cover practical scenarios where TQA models need to process tables without header annotations in the inference phase or tables of different types. To address this issue, we construct a new TQA dataset with implicit and multi-type table structures, named IM-TQA, which not only requires the model to answer questions without header annotations beforehand but also to handle multi-type tables including previously neglected complex tables. We investigate the performance of recent methods on our dataset and find that existing methods struggle in processing implicit and multi-type table structures. Correspondingly, we propose an RGCN-RCI framework outperforming recent baselines. We will release our dataset to facilitate future research.

Paper Type: long

Research Area: Question Answering