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, non-standard 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.

翻译：文本到SQL解析与端到端表格问答是基于表格的问答任务的两种主要方法。尽管在多个基准测试中取得了成功，但二者尚未得到系统比较，其协同潜力仍有待探索。本文通过在基准数据集上评估最先进的模型，揭示了二者不同的优势与局限：文本到SQL方法在处理涉及算术运算和长表格的问题时表现更优；端到端表格问答则在处理模糊问题、非标准表结构及复杂表格内容方面更具优势。为融合双方优势，我们提出一种协同式表格问答方法，通过答案选择机制集成不同模型，该方法不依赖于任何特定模型类型。进一步的实验验证表明，基于特征或大语言模型的答案选择器集成策略均能显著提升模型性能，超越单一模型的表现。