RETQA: A Large-Scale Open-Domain Tabular Question Answering Dataset for Real Estate Sector
This addresses the problem of limited automated question-answering systems for real estate professionals by providing a new dataset and method, though it is incremental as it builds on existing tabular QA research.
The authors tackled the lack of specialized datasets for tabular question answering in real estate by introducing RETQA, a large-scale Chinese dataset with 4,932 tables and 20,762 question-answer pairs, and proposed the SLUTQA framework, which significantly improved performance through in-context learning.
The real estate market relies heavily on structured data, such as property details, market trends, and price fluctuations. However, the lack of specialized Tabular Question Answering datasets in this domain limits the development of automated question-answering systems. To fill this gap, we introduce RETQA, the first large-scale open-domain Chinese Tabular Question Answering dataset for Real Estate. RETQA comprises 4,932 tables and 20,762 question-answer pairs across 16 sub-fields within three major domains: property information, real estate company finance information and land auction information. Compared with existing tabular question answering datasets, RETQA poses greater challenges due to three key factors: long-table structures, open-domain retrieval, and multi-domain queries. To tackle these challenges, we propose the SLUTQA framework, which integrates large language models with spoken language understanding tasks to enhance retrieval and answering accuracy. Extensive experiments demonstrate that SLUTQA significantly improves the performance of large language models on RETQA by in-context learning. RETQA and SLUTQA provide essential resources for advancing tabular question answering research in the real estate domain, addressing critical challenges in open-domain and long-table question-answering. The dataset and code are publicly available at \url{https://github.com/jensen-w/RETQA}.