UnifiedSKG: Unifying And Multi-Tasking Structured Knowledge ...

Computer Science > Computation and Language arXiv:2201.05966 (cs) [Submitted on 16 Jan 2022 (v1), last revised 18 Oct 2022 (this version, v3)] Title:UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models Authors: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, Rui Zhang, Noah A. Smith, Luke Zettlemoyer, Tao Yu View a PDF of the paper titled UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models, by Tianbao Xie and 22 other authors View PDF

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 this https URL.

Comments:	EMNLP 2022
Subjects:	Computation and Language (cs.CL)
Cite as:	arXiv:2201.05966 [cs.CL]
(or arXiv:2201.05966v3 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2201.05966 Focus to learn more arXiv-issued DOI via DataCite

Submission history

From: Chen Henry Wu [view email] [v1] Sun, 16 Jan 2022 04:36:18 UTC (4,976 KB) [v2] Thu, 20 Jan 2022 03:20:45 UTC (4,976 KB) [v3] Tue, 18 Oct 2022 15:56:01 UTC (4,983 KB) Full-text links:

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