SynTQA: Synergistic Table-based Question Answering via Mixture of Text-to-SQL and E2E TQA
Siyue Zhang, Anh Tuan Luu, Zhao Chen · 2024
Text-to-SQL parsing and end-to-end question answering (E2E TQA) are two main approaches for Table-based Question Answering task.Despite success on multiple benchmarks, they have yet to be compared and their synergy remains unexplored.In this paper, we identify different strengths and weaknesses through evaluating state-of-the-art models on benchmark datasets: Text-to-SQL demonstrates superiority in handling questions involving arithmetic operations and long tables; E2E TQA excels in addressing ambiguous questions, nonstandard table schema, and complex table contents.To combine both strengths, we propose a Synergistic Table-based Question Answering approach that integrate different models via answer selection, which is agnostic to any model types.Further experiments validate that ensembling models by either feature-based or LLM-based answer selector significantly improves the performance over individual models.