Substructural Regularization With Data-Sensitive Granularity for Sequence Transfer Learning.

Area of Science:

• Natural Language Processing
• Machine Learning
• Computational Linguistics

Background:

• Transfer learning is crucial for adapting models to new text domains, especially from social media.
• Existing methods may struggle to preserve essential target domain features with limited labeled data.

Purpose of the Study:

• To propose a novel substructural regularization transfer learning model (STLM).
• To address the challenge of preserving target domain features at a substructural level, considering labeled dataset size.

Main Methods:

• Developed a data-sensitive granularity approach for transfer learning.
• Integrated Hidden Markov Models and regularization theory within STLM.
• Utilized relative entropy to measure and penalize substructural dissimilarity between domains.

Main Results:

• STLM effectively preserves target domain substructures while utilizing both source and target domain observations.
• Achieved efficient model estimation through an analytical solution.
• Demonstrated performance improvements in part-of-speech tagging experiments across different domain combinations (Brown and Twitter corpora).

Conclusions:

• The proposed STLM offers a robust solution for sequence transfer learning, particularly in data-scarce scenarios.
• The model's ability to preserve substructural information enhances its applicability to diverse text domains.
• Experimental results validate the model's effectiveness and efficiency.