Knowledge Enhanced Pretrained Language Models: A Compreshensive Survey

• URL: http://arxiv.org/abs/2110.08455v1
• Date: Sat, 16 Oct 2021 03:27:56 GMT
• Title: Knowledge Enhanced Pretrained Language Models: A Compreshensive Survey
• Authors: Xiaokai Wei, Shen Wang, Dejiao Zhang, Parminder Bhatia, Andrew Arnold
• Abstract summary: Pretrained Language Models (PLM) have established a new paradigm through learning informative representations on large-scale text corpus. This new paradigm has revolutionized the entire field of natural language processing, and set the new state-of-the-art performance for a wide variety of NLP tasks. To address this issue, integrating knowledge into PLMs have recently become a very active research area and a variety of approaches have been developed.
• Score: 8.427521246916463
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Pretrained Language Models (PLM) have established a new paradigm through learning informative contextualized representations on large-scale text corpus. This new paradigm has revolutionized the entire field of natural language processing, and set the new state-of-the-art performance for a wide variety of NLP tasks. However, though PLMs could store certain knowledge/facts from training corpus, their knowledge awareness is still far from satisfactory. To address this issue, integrating knowledge into PLMs have recently become a very active research area and a variety of approaches have been developed. In this paper, we provide a comprehensive survey of the literature on this emerging and fast-growing field - Knowledge Enhanced Pretrained Language Models (KE-PLMs). We introduce three taxonomies to categorize existing work. Besides, we also survey the various NLU and NLG applications on which KE-PLM has demonstrated superior performance over vanilla PLMs. Finally, we discuss challenges that face KE-PLMs and also promising directions for future research.

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