Leveraging Data Recasting to Enhance Tabular Reasoning

Aashna Jena, Vivek Gupta, Manish Shrivastava, Julian Eisenschlos

Abstract
Creating challenging tabular inference data is essential for learning complex reasoning. Prior work has mostly relied on two data generation strategies. The first is human annotation, which yields linguistically diverse data but is difficult to scale. The second category for creation is synthetic generation, which is scalable and cost effective but lacks inventiveness. In this research, we present a framework for semi-automatically recasting existing tabular data to make use of the benefits of both approaches. We utilize our framework to build tabular NLI instances from five datasets that were initially intended for tasks like table2text creation, tabular Q/A, and semantic parsing. We demonstrate that recasted data could be used as evaluation benchmarks as well as augmentation data to enhance performance on tabular NLI tasks. Furthermore, we investigate the effectiveness of models trained on recasted data in the zero-shot scenario, and analyse trends in performance across different recasted datasets types.
Anthology ID:
2022.findings-emnlp.328
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2022
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4483–4496
Language:
URL:
https://aclanthology.org/2022.findings-emnlp.328
DOI:
Bibkey:
Cite (ACL):
Aashna Jena, Vivek Gupta, Manish Shrivastava, and Julian Eisenschlos. 2022. Leveraging Data Recasting to Enhance Tabular Reasoning. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 4483–4496, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Leveraging Data Recasting to Enhance Tabular Reasoning (Jena et al., Findings 2022)
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