This approach demonstrates improved accuracy in table cell prediction, suggesting effective segmentation and retrieval methods.
In this paper, we propose a three-stage method for the U4 TableQA task. The method first analyzes and segments the target table into header and data cell sections using a machine learning classifier. Then, it generates natural language descriptions for each data cell using sentence templates based on the table structure. Finally, it retrieves relevant sentences matching the input question from the generated sentence set to form the TableQA result. This approach is also extended to the Table Retrieval task. Evaluation experiments showed that the Table Retrieval task achieved an accuracy of 0.3569, whereas for the TableQA task, the accuracy of cell_id prediction was 0.7797, and the value prediction was 0.7168.
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Takasago et al. (2025) studied this question.
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