HiTab: A Hierarchical Table Dataset for Question Answering and Natural Language Generation

• URL: http://arxiv.org/abs/2108.06712v1
• Date: Sun, 15 Aug 2021 10:14:21 GMT
• Title: HiTab: A Hierarchical Table Dataset for Question Answering and Natural Language Generation
• Authors: Zhoujun Cheng, Haoyu Dong, Zhiruo Wang, Ran Jia, Jiaqi Guo, Yan Gao, Shi Han, Jian-Guang Lou, Dongmei Zhang
• Abstract summary: Hierarchical tables challenge existing methods by hierarchical indexing, as well as implicit relationships of calculation and semantics. This work presents HiTab, a free and open dataset for the research community to study question answering (QA) and natural language generation (NLG) over hierarchical tables.
• Score: 35.73434495391091
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Tables are often created with hierarchies, but existing works on table reasoning mainly focus on flat tables and neglect hierarchical tables. Hierarchical tables challenge existing methods by hierarchical indexing, as well as implicit relationships of calculation and semantics. This work presents HiTab, a free and open dataset for the research community to study question answering (QA) and natural language generation (NLG) over hierarchical tables. HiTab is a cross-domain dataset constructed from a wealth of statistical reports and Wikipedia pages, and has unique characteristics: (1) nearly all tables are hierarchical, and (2) both target sentences for NLG and questions for QA are revised from high-quality descriptions in statistical reports that are meaningful and diverse. (3) HiTab provides fine-grained annotations on both entity and quantity alignment. Targeting hierarchical structure, we devise a novel hierarchy-aware logical form for symbolic reasoning over tables, which shows high effectiveness. Then given annotations of entity and quantity alignment, we propose partially supervised training, which helps models to largely reduce spurious predictions in the QA task. In the NLG task, we find that entity and quantity alignment also helps NLG models to generate better results in a conditional generation setting. Experiment results of state-of-the-art baselines suggest that this dataset presents a strong challenge and a valuable benchmark for future research.

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