ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examples

Yilun Zhao, Linyong Nan, Zhenting Qi, Rui Zhang, Dragomir Radev


Abstract
Reasoning over tabular data requires both table structure understanding and a broad set of table reasoning skills. Current models with table-specific architectures and pre-training methods perform well on understanding table structures, but they still struggle with tasks that require various table reasoning skills. In this work, we develop ReasTAP to show that high-level table reasoning skills can be injected into models during pre-training without a complex table-specific architecture design. We define 7 table reasoning skills, such as numerical operation, temporal comparison, and conjunction. Each reasoning skill is associated with one example generator, which synthesizes questions over semi-structured tables according to the sampled templates. We model the table pre-training task as a sequence generation task and pre-train ReasTAP to generate precise answers of the synthetic examples. ReasTAP is evaluated on four benchmarks covering three downstream tasks including 1) WikiSQL-Weak and WikiTQ for Table Question Answering, 2) TabFact for Table Fact Verification, and 3) LogicNLG for Faithful Table-to-Text Generation. Experimental results demonstrate that ReasTAP achieves new state-of-the-art results on all of them and delivers a significant improvement under low-resource setting. Our code is publicly available at https://github.com/Yale-LILY/ReasTAP.
Anthology ID:
2022.emnlp-main.615
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
9006–9018
Language:
URL:
https://aclanthology.org/2022.emnlp-main.615
DOI:
10.18653/v1/2022.emnlp-main.615
Bibkey:
Cite (ACL):
Yilun Zhao, Linyong Nan, Zhenting Qi, Rui Zhang, and Dragomir Radev. 2022. ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examples. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 9006–9018, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examples (Zhao et al., EMNLP 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.emnlp-main.615.pdf