Reproduce “ERNIE: Enhanced Language Representation with Informative Entities” on OpenResearch
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About this paper
Zhengyan Zhang, Xu Han, Zhiyuan Liu, Xin Jiang, Maosong Sun, Qun Liu
The ERNIE model combines pre-trained language representations with structured knowledge from knowledge graphs to enhance language understanding. It demonstrates improved performance on knowledge-intensive NLP tasks such as entity typing and relation classification while preserving capabilities on general natural language understanding benchmarks.
Key insights
- Integrating structured knowledge from KGs into pre-trained language models improves performance on knowledge-intensive tasks while maintaining general language understanding.
- Both the denoising entity auto-encoder pre-training task and explicit entity input during fine-tuning are essential and complementary for optimal knowledge injection.
- External knowledge can regularize the learning process, leading to more robust models, especially in scenarios with noisy labels or limited training data.
Summary by alphaXiv.
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