Grounded Keys-to-Text Generation: Towards Factual Open-Ended Generation

Faeze Brahman, Baolin Peng, Michel Galley, Sudha Rao, Bill Dolan, Snigdha Chaturvedi, Jianfeng Gao


Abstract
Large pre-trained language models have recently enabled open-ended generation frameworks (e.g., prompt-to-text NLG) to tackle a variety of tasks going beyond the traditional data-to-text generation. While this framework is more general, it is under-specified and often leads to a lack of controllability restricting their real-world usage. We propose a new grounded keys-to-text generation task: the task is to generate a factual description about an entity given a set of guiding keys, and grounding passages. To address this task, we introduce a new dataset, called EntDeGen. Inspired by recent QA-based evaluation measures, we propose an automatic metric, MAFE, for factual correctness of generated descriptions. Our EntDescriptor model is equipped with strong rankers to fetch helpful passages and generate entity descriptions. Experimental result shows a good correlation (60.14) between our proposed metric and human judgments of factuality. Our rankers significantly improved the factual correctness of generated descriptions (15.95% and 34.51% relative gains in recall and precision). Finally, our ablation study highlights the benefit of combining keys and groundings.
Anthology ID:
2022.findings-emnlp.547
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2022
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7397–7413
Language:
URL:
https://aclanthology.org/2022.findings-emnlp.547
DOI:
10.18653/v1/2022.findings-emnlp.547
Bibkey:
Cite (ACL):
Faeze Brahman, Baolin Peng, Michel Galley, Sudha Rao, Bill Dolan, Snigdha Chaturvedi, and Jianfeng Gao. 2022. Grounded Keys-to-Text Generation: Towards Factual Open-Ended Generation. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 7397–7413, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Grounded Keys-to-Text Generation: Towards Factual Open-Ended Generation (Brahman et al., Findings 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.findings-emnlp.547.pdf
Video:
 https://aclanthology.org/2022.findings-emnlp.547.mp4