Pre-Training a Graph Recurrent Network for Text Understanding
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a novel graph recurrent network for language model pre-training, offering linear time complexity and improved sentence representation. The new model achieves comparable performance to Transformers with greater efficiency and more diverse representations.
Area Of Science
- Natural Language Processing
- Deep Learning Architectures
- Computational Linguistics
Background
- Transformer models dominate Natural Language Processing (NLP) but suffer from quadratic time complexity and limited sentence-level expressiveness due to reliance on special tokens.
- Existing alternatives like CNNs, MLPs, and SSMs have been explored, but limitations in efficiency and representation quality persist.
Purpose Of The Study
- To propose a novel graph recurrent network (GRN) for language model pre-training that addresses the limitations of Transformer architectures.
- To achieve comparable performance to existing models with enhanced inference efficiency and improved representation quality.
Main Methods
- Developed a graph recurrent network with linear time complexity for pre-training language models.
- Constructed a graph structure for each sequence, enabling local token-level communications.
- Introduced a detached sentence-level representation independent of normal tokens.
Main Results
- The proposed GRN achieved comparable performance to Transformer-based models on English and Chinese text understanding tasks.
- Demonstrated significantly higher inference efficiency compared to existing pre-trained models.
- Discovered that the GRN generates more diverse and uniform representations, mitigating issues like representation degradation.
Conclusions
- The graph recurrent network offers a viable and efficient alternative to Transformer models for language model pre-training.
- The GRN's architecture effectively addresses computational cost and representation limitations, leading to improved performance and efficiency.
- The model's ability to generate diverse and uniform representations enhances its applicability in various NLP tasks.
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