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 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 (analyses) 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 original, meaningful, and diverse descriptive sentences authored
by analysts and professions of reports. (3) to reveal complex numerical
reasoning in statistical analyses, we provide fine-grained annotations of
entity and quantity alignment. HiTab provides 10,686 QA pairs and descriptive
sentences with well-annotated quantity and entity alignment on 3,597 tables
with broad coverage of table hierarchies and numerical reasoning types.
Targeting hierarchical structure, we devise a novel hierarchy-aware logical
form for symbolic reasoning over tables, which shows high effectiveness.
Targeting complex numerical reasoning, we propose partially supervised training
given annotations of entity and quantity alignment, 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.

这项研究提出了 HiTab 数据集，用于对分层表格进行问题回答和自然语言生成的学习和研究，并提供了包含数量和实体对齐注释的描述性句子和问题，挑战了现有方法的分层索引、计算和语义相关性问题，是未来研究的有价值的基准。