Language Model Pre-Training with Sparse Latent Typing

• URL: http://arxiv.org/abs/2210.12582v2
• Date: Wed, 26 Oct 2022 22:41:30 GMT
• Title: Language Model Pre-Training with Sparse Latent Typing
• Authors: Liliang Ren, Zixuan Zhang, Han Wang, Clare R. Voss, Chengxiang Zhai, Heng Ji
• Abstract summary: We propose a new pre-training objective, Sparse Latent Typing, which enables the model to sparsely extract sentence-level keywords with diverse latent types. Experimental results show that our model is able to learn interpretable latent type categories in a self-supervised manner without using any external knowledge.
• Score: 66.75786739499604
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern large-scale Pre-trained Language Models (PLMs) have achieved tremendous success on a wide range of downstream tasks. However, most of the LM pre-training objectives only focus on text reconstruction, but have not sought to learn latent-level interpretable representations of sentences. In this paper, we manage to push the language models to obtain a deeper understanding of sentences by proposing a new pre-training objective, Sparse Latent Typing, which enables the model to sparsely extract sentence-level keywords with diverse latent types. Experimental results show that our model is able to learn interpretable latent type categories in a self-supervised manner without using any external knowledge. Besides, the language model pre-trained with such an objective also significantly improves Information Extraction related downstream tasks in both supervised and few-shot settings. Our code is publicly available at: https://github.com/renll/SparseLT.

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