RoSeq: Robust Sequence Labeling.

Area of Science:

• Natural Language Processing
• Machine Learning
• Data Science

Background:

• Named Entity Recognition (NER) commonly faces challenges with imbalanced labels and noisy training data.
• Existing methods often overlook these critical issues, impacting model performance.
• Robustness in sequence labeling models is crucial for real-world applications.

Purpose of the Study:

• To propose a novel method, Robust Sequence Labeling (RoSeq), addressing label imbalance and noisy data in NER.
• To enhance the performance and generalization capabilities of sequence labeling models.
• To establish new state-of-the-art benchmarks on challenging NER datasets.

Main Methods:

• Incorporation of label statistics into a novel Conditional Random Field (CRF) loss function to mitigate label imbalance.
• Design of an auxiliary loss function to down-weight easy tokens, further addressing label imbalance.
• Application of an adversarial training strategy to improve model generalization against noisy data.

Main Results:

• RoSeq achieved state-of-the-art performance on CoNLL-2002 Dutch (88.07%) and Spanish (87.33%).
• Significant improvements were observed on Twitter NER datasets: WNUT-2016 (52.94%) and WNUT-2017 (43.03%).
• The model demonstrated high efficacy without reliance on additional training data.

Conclusions:

• The proposed RoSeq method effectively handles label imbalance and noisy data in sequence labeling tasks.
• RoSeq sets new performance benchmarks for Named Entity Recognition, particularly on specialized and challenging datasets.
• The approach offers a robust solution for NER systems operating in realistic, imperfect data conditions.