Knowledgeable Salient Span Mask for Enhancing Language Models as Knowledge Base

• URL: http://arxiv.org/abs/2204.07994v2
• Date: Wed, 11 Oct 2023 05:12:10 GMT
• Title: Knowledgeable Salient Span Mask for Enhancing Language Models as Knowledge Base
• Authors: Cunxiang Wang, Fuli Luo, Yanyang Li, Runxin Xu, Fei Huang and Yue Zhang
• Abstract summary: We develop two solutions to help the model learn more knowledge from unstructured text in a fully self-supervised manner. To our best knowledge, we are the first to explore fully self-supervised learning of knowledge in continual pre-training.
• Score: 51.55027623439027
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Pre-trained language models (PLMs) like BERT have made significant progress in various downstream NLP tasks. However, by asking models to do cloze-style tests, recent work finds that PLMs are short in acquiring knowledge from unstructured text. To understand the internal behaviour of PLMs in retrieving knowledge, we first define knowledge-baring (K-B) tokens and knowledge-free (K-F) tokens for unstructured text and ask professional annotators to label some samples manually. Then, we find that PLMs are more likely to give wrong predictions on K-B tokens and attend less attention to those tokens inside the self-attention module. Based on these observations, we develop two solutions to help the model learn more knowledge from unstructured text in a fully self-supervised manner. Experiments on knowledge-intensive tasks show the effectiveness of the proposed methods. To our best knowledge, we are the first to explore fully self-supervised learning of knowledge in continual pre-training.

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