KGPT: Knowledge-Grounded Pre-Training for Data-to-Text Generation

Wenhu Chen, Yu Su, Xifeng Yan, William Yang Wang


Abstract
Data-to-text generation has recently attracted substantial interests due to its wide applications. Existing methods have shown impressive performance on an array of tasks. However, they rely on a significant amount of labeled data for each task, which is costly to acquire and thus limits their application to new tasks and domains. In this paper, we propose to leverage pre-training and transfer learning to address this issue. We propose a knowledge-grounded pre-training (KGPT), which consists of two parts, 1) a general knowledge-grounded generation model to generate knowledge-enriched text. 2) a pre-training paradigm on a massive knowledge-grounded text corpus crawled from the web. The pre-trained model can be fine-tuned on various data-to-text generation tasks to generate task-specific text. We adopt three settings, namely fully-supervised, zero-shot, few-shot to evaluate its effectiveness. Under the fully-supervised setting, our model can achieve remarkable gains over the known baselines. Under zero-shot setting, our model without seeing any examples achieves over 30 ROUGE-L on WebNLG while all other baselines fail. Under the few-shot setting, our model only needs about one-fifteenth as many labeled examples to achieve the same level of performance as baseline models. These experiments consistently prove the strong generalization ability of our proposed framework.
Anthology ID:
2020.emnlp-main.697
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
8635–8648
Language:
URL:
https://aclanthology.org/2020.emnlp-main.697
DOI:
10.18653/v1/2020.emnlp-main.697
Bibkey:
Cite (ACL):
Wenhu Chen, Yu Su, Xifeng Yan, and William Yang Wang. 2020. KGPT: Knowledge-Grounded Pre-Training for Data-to-Text Generation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 8635–8648, Online. Association for Computational Linguistics.
Cite (Informal):
KGPT: Knowledge-Grounded Pre-Training for Data-to-Text Generation (Chen et al., EMNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.697.pdf
Video:
 https://slideslive.com/38938913
Code
 wenhuchen/KGPT
Data
WebNLGWikiBio