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arXiv · 2305.01624

UNTER: A Unified Knowledge Interface for Enhancing Pre-trained Language Models

Deming Ye·Yankai Lin·Zhengyan Zhang·Maosong Sun
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Abstract

Recent research demonstrates that external knowledge injection can advance pre-trained language models (PLMs) in a variety of downstream NLP tasks. However, existing knowledge injection methods are either applicable to structured knowledge or unstructured knowledge, lacking a unified usage. In this paper, we propose a UNified knowledge inTERface, UNTER, to provide a unified perspective to exploit both structured knowledge and unstructured knowledge. In UNTER, we adopt the decoder as a unified knowledge interface, aligning span representations obtained from the encoder with their corresponding knowledge. This approach enables the encoder to uniformly invoke span-related knowledge from its parameters for downstream applications. Experimental results show that, with both forms of knowledge injected, UNTER gains continuous improvements on a series of knowledge-driven NLP tasks, including entity typing, named entity recognition and relation extraction, especially in low-resource scenarios.

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Deming Ye, Yankai Lin, Zhengyan Zhang, Maosong Sun. 2023-05-02. UNTER: A Unified Knowledge Interface for Enhancing Pre-trained Language Models. https://arxiv.org/abs/2305.01624

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