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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T SynTQA: Synergistic Table-based Question Answering via Mixture of Text-to-SQL and E2E TQA
%A Zhang, Siyue
%A Luu, Anh Tuan
%A Zhao, Chen
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F zhang-etal-2024-syntqa
%X 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.
%R 10.18653/v1/2024.findings-emnlp.131
%U https://aclanthology.org/2024.findings-emnlp.131/
%U https://doi.org/10.18653/v1/2024.findings-emnlp.131
%P 2352-2364

Markdown (Informal)

[SynTQA: Synergistic Table-based Question Answering via Mixture of Text-to-SQL and E2E TQA](https://aclanthology.org/2024.findings-emnlp.131/) (Zhang et al., Findings 2024)

• SynTQA: Synergistic Table-based Question Answering via Mixture of Text-to-SQL and E2E TQA (Zhang et al., Findings 2024)

ACL

• Siyue Zhang, Anh Tuan Luu, and Chen Zhao. 2024. SynTQA: Synergistic Table-based Question Answering via Mixture of Text-to-SQL and E2E TQA. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 2352–2364, Miami, Florida, USA. Association for Computational Linguistics.