Few-Shot Table-to-Text Generation with Prototype Memory

Yixuan Su, Zaiqiao Meng, Simon Baker, Nigel Collier


Abstract
Neural table-to-text generation models have achieved remarkable progress on an array of tasks. However, due to the data-hungry nature of neural models, their performances strongly rely on large-scale training examples, limiting their applicability in real-world applications. To address this, we propose a new framework: Prototype-to-Generate (P2G), for table-to-text generation under the few-shot scenario. The proposed framework utilizes the retrieved prototypes, which are jointly selected by an IR system and a novel prototype selector to help the model bridging the structural gap between tables and texts. Experimental results on three benchmark datasets with three state-of-the-art models demonstrate that the proposed framework significantly improves the model performance across various evaluation metrics.
Anthology ID:
2021.findings-emnlp.77
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2021
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Venue:
Findings
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
910–917
Language:
URL:
https://aclanthology.org/2021.findings-emnlp.77
DOI:
10.18653/v1/2021.findings-emnlp.77
Bibkey:
Cite (ACL):
Yixuan Su, Zaiqiao Meng, Simon Baker, and Nigel Collier. 2021. Few-Shot Table-to-Text Generation with Prototype Memory. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 910–917, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Few-Shot Table-to-Text Generation with Prototype Memory (Su et al., Findings 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.findings-emnlp.77.pdf
Video:
 https://aclanthology.org/2021.findings-emnlp.77.mp4
Code
 yxuansu/few-shot-table-to-text-generation