@inproceedings{su-etal-2021-shot-table,
title = "Few-Shot Table-to-Text Generation with Prototype Memory",
author = "Su, Yixuan and
Meng, Zaiqiao and
Baker, Simon and
Collier, Nigel",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.77",
doi = "10.18653/v1/2021.findings-emnlp.77",
pages = "910--917",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Few-Shot Table-to-Text Generation with Prototype Memory
%A Su, Yixuan
%A Meng, Zaiqiao
%A Baker, Simon
%A Collier, Nigel
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F su-etal-2021-shot-table
%X 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.
%R 10.18653/v1/2021.findings-emnlp.77
%U https://aclanthology.org/2021.findings-emnlp.77
%U https://doi.org/10.18653/v1/2021.findings-emnlp.77
%P 910-917
Markdown (Informal)
[Few-Shot Table-to-Text Generation with Prototype Memory](https://aclanthology.org/2021.findings-emnlp.77) (Su et al., Findings 2021)
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.