Dual Adversarial Transfer for Sequence Labeling.

Area of Science:

• Natural Language Processing
• Machine Learning
• Artificial Intelligence

Background:

• Sequence labeling tasks are crucial in NLP but often suffer from data scarcity and imbalance.
• Existing methods struggle with effective feature fusion between high and low-resource datasets.
• Noisy and imbalanced training data hinder model generalization in sequence labeling.

Purpose of the Study:

• To propose a novel architecture, Dual Adversarial Transfer Network (DATNet), for sequence labeling.
• To enhance feature fusion between high and low-resource data using two variants: DATNet-F and DATNet-P.
• To improve model generalization on noisy and imbalanced data through adversarial training.

Main Methods:

• Introduced the Dual Adversarial Transfer Network (DATNet) architecture with variants DATNet-F and DATNet-P.
• Proposed a Generalized Resource-Adversarial Discriminator (GRAD) to handle noisy and imbalanced data.
• Employed adversarial training to boost model generalization capabilities.

Main Results:

• Achieved state-of-the-art performances on multiple benchmark datasets (CoNLL, Twitter, PTB-WSJ, OntoNotes, Universal Dependencies).
• Demonstrated significant improvements, particularly for low-resource data, without relying on hand-crafted features.
• Validated the effectiveness of DATNet components across diverse domains and languages.

Conclusions:

• DATNet offers an effective approach for sequence labeling, especially in low-resource scenarios.
• The proposed GRAD and adversarial training significantly enhance model generalization.
• The architecture achieves superior performance on Named Entity Recognition (NER), Part-of-Speech (POS) Tagging, and Chunking tasks.