@inproceedings{xie-etal-2022-unifiedskg,
title = "{U}nified{SKG}: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models",
author = "Xie, Tianbao and
Wu, Chen Henry and
Shi, Peng and
Zhong, Ruiqi and
Scholak, Torsten and
Yasunaga, Michihiro and
Wu, Chien-Sheng and
Zhong, Ming and
Yin, Pengcheng and
Wang, Sida I. and
Zhong, Victor and
Wang, Bailin and
Li, Chengzu and
Boyle, Connor and
Ni, Ansong and
Yao, Ziyu and
Radev, Dragomir and
Xiong, Caiming and
Kong, Lingpeng and
Zhang, Rui and
Smith, Noah A. and
Zettlemoyer, Luke and
Yu, Tao",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.39",
doi = "10.18653/v1/2022.emnlp-main.39",
pages = "602--631",
abstract = "Structured knowledge grounding (SKG) leverages structured knowledge to complete user requests, such as semantic parsing over databases and question answering over knowledge bases. Since the inputs and outputs of SKG tasks are heterogeneous, they have been studied separately by different communities, which limits systematic and compatible research on SKG. In this paper, we overcome this limitation by proposing the UnifiedSKG framework, which unifies 21 SKG tasks into a text-to-text format, aiming to promote systematic SKG research, instead of being exclusive to a single task, domain, or dataset. We use UnifiedSKG to benchmark T5 with different sizes and show that T5, with simple modifications when necessary, achieves state-of-the-art performance on almost all of the 21 tasks. We further demonstrate that multi-task prefix-tuning improves the performance on most tasks, largely improving the overall performance. UnifiedSKG also facilitates the investigation of zero-shot and few-shot learning, and we show that T0, GPT-3, and Codex struggle in zero-shot and few-shot learning for SKG. We also use UnifiedSKG to conduct a series of controlled experiments on structured knowledge encoding variants across SKG tasks. UnifiedSKG is easily extensible to more tasks, and it is open-sourced at https://github.com/hkunlp/unifiedskg.",
}
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<abstract>Structured knowledge grounding (SKG) leverages structured knowledge to complete user requests, such as semantic parsing over databases and question answering over knowledge bases. Since the inputs and outputs of SKG tasks are heterogeneous, they have been studied separately by different communities, which limits systematic and compatible research on SKG. In this paper, we overcome this limitation by proposing the UnifiedSKG framework, which unifies 21 SKG tasks into a text-to-text format, aiming to promote systematic SKG research, instead of being exclusive to a single task, domain, or dataset. We use UnifiedSKG to benchmark T5 with different sizes and show that T5, with simple modifications when necessary, achieves state-of-the-art performance on almost all of the 21 tasks. We further demonstrate that multi-task prefix-tuning improves the performance on most tasks, largely improving the overall performance. UnifiedSKG also facilitates the investigation of zero-shot and few-shot learning, and we show that T0, GPT-3, and Codex struggle in zero-shot and few-shot learning for SKG. We also use UnifiedSKG to conduct a series of controlled experiments on structured knowledge encoding variants across SKG tasks. UnifiedSKG is easily extensible to more tasks, and it is open-sourced at https://github.com/hkunlp/unifiedskg.</abstract>
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%0 Conference Proceedings
%T UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models
%A Xie, Tianbao
%A Wu, Chen Henry
%A Shi, Peng
%A Zhong, Ruiqi
%A Scholak, Torsten
%A Yasunaga, Michihiro
%A Wu, Chien-Sheng
%A Zhong, Ming
%A Yin, Pengcheng
%A Wang, Sida I.
%A Zhong, Victor
%A Wang, Bailin
%A Li, Chengzu
%A Boyle, Connor
%A Ni, Ansong
%A Yao, Ziyu
%A Radev, Dragomir
%A Xiong, Caiming
%A Kong, Lingpeng
%A Zhang, Rui
%A Smith, Noah A.
%A Zettlemoyer, Luke
%A Yu, Tao
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F xie-etal-2022-unifiedskg
%X Structured knowledge grounding (SKG) leverages structured knowledge to complete user requests, such as semantic parsing over databases and question answering over knowledge bases. Since the inputs and outputs of SKG tasks are heterogeneous, they have been studied separately by different communities, which limits systematic and compatible research on SKG. In this paper, we overcome this limitation by proposing the UnifiedSKG framework, which unifies 21 SKG tasks into a text-to-text format, aiming to promote systematic SKG research, instead of being exclusive to a single task, domain, or dataset. We use UnifiedSKG to benchmark T5 with different sizes and show that T5, with simple modifications when necessary, achieves state-of-the-art performance on almost all of the 21 tasks. We further demonstrate that multi-task prefix-tuning improves the performance on most tasks, largely improving the overall performance. UnifiedSKG also facilitates the investigation of zero-shot and few-shot learning, and we show that T0, GPT-3, and Codex struggle in zero-shot and few-shot learning for SKG. We also use UnifiedSKG to conduct a series of controlled experiments on structured knowledge encoding variants across SKG tasks. UnifiedSKG is easily extensible to more tasks, and it is open-sourced at https://github.com/hkunlp/unifiedskg.
%R 10.18653/v1/2022.emnlp-main.39
%U https://aclanthology.org/2022.emnlp-main.39
%U https://doi.org/10.18653/v1/2022.emnlp-main.39
%P 602-631
Markdown (Informal)
[UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models](https://aclanthology.org/2022.emnlp-main.39) (Xie et al., EMNLP 2022)
ACL
- Tianbao Xie, Chen Henry Wu, Peng Shi, Ruiqi Zhong, Torsten Scholak, Michihiro Yasunaga, Chien-Sheng Wu, Ming Zhong, Pengcheng Yin, Sida I. Wang, Victor Zhong, Bailin Wang, Chengzu Li, Connor Boyle, Ansong Ni, Ziyu Yao, Dragomir Radev, Caiming Xiong, Lingpeng Kong, et al.. 2022. UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 602–631, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.