KgPLM: Knowledge-guided Language Model Pre-training via Generative and Discriminative Learning

• URL: http://arxiv.org/abs/2012.03551v1
• Date: Mon, 7 Dec 2020 09:39:25 GMT
• Title: KgPLM: Knowledge-guided Language Model Pre-training via Generative and Discriminative Learning
• Authors: Bin He, Xin Jiang, Jinghui Xiao, Qun Liu
• Abstract summary: We present a language model pre-training framework guided by factual knowledge completion and verification. Experimental results on LAMA, a set of zero-shot cloze-style question answering tasks, show that our model contains richer factual knowledge than the conventional pre-trained language models.
• Score: 45.067001062192844
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent studies on pre-trained language models have demonstrated their ability to capture factual knowledge and applications in knowledge-aware downstream tasks. In this work, we present a language model pre-training framework guided by factual knowledge completion and verification, and use the generative and discriminative approaches cooperatively to learn the model. Particularly, we investigate two learning schemes, named two-tower scheme and pipeline scheme, in training the generator and discriminator with shared parameter. Experimental results on LAMA, a set of zero-shot cloze-style question answering tasks, show that our model contains richer factual knowledge than the conventional pre-trained language models. Furthermore, when fine-tuned and evaluated on the MRQA shared tasks which consists of several machine reading comprehension datasets, our model achieves the state-of-the-art performance, and gains large improvements on NewsQA (+1.26 F1) and TriviaQA (+1.56 F1) over RoBERTa.

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