# A Hybrid Semantic Parsing Approach for Tabular Data Analysis

arXiv: https://arxiv.org/abs/1910.10363 · HTML: https://mlsanity.com/paper/1910.10363/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2019.

## Built on by (most cited first, 2 of 2)

- [TaCube: Pre-computing Data Cubes for Answering Numerical-Reasoning Questions over Tabular Data](https://mlsanity.com/paper/2205.12682/) (2022)
- [KET-QA: A Dataset for Knowledge Enhanced Table Question Answering](https://mlsanity.com/paper/2405.08099/) (2024)

## Abstract (arXiv)

This paper presents a novel approach to translating natural language questions to SQL queries for given tables, which meets three requirements as a real-world data analysis application: cross-domain, multilingualism and enabling quick-start. Our proposed approach consists of: (1) a novel data abstraction step before the parser to make parsing table-agnosticism; (2) a set of semantic rules for parsing abstracted data-analysis questions to intermediate logic forms as tree derivations to reduce the search space; (3) a neural-based model as a local scoring function on a span-based semantic parser for structured optimization and efficient inference. Experiments show that our approach outperforms state-of-the-art algorithms on a large open benchmark dataset WikiSQL. We also achieve promising results on a small dataset for more complex queries in both English and Chinese, which demonstrates our language expansion and quick-start ability.