lrl-nlp-techniques

Low-Resource NLP Techniques for Somali

Project Context

Language: Somali (Cushitic language family) Task: Dialect classification (Northern, Southern, Central) Challenge: Limited labeled training data Approach: Low-resource NLP techniques + transfer learning

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Data Scarcity Strategies

1. Cross-Lingual Transfer

Approach: Leverage high-resource languages with linguistic similarity

For Somali:

• Use multilingual models (mBERT, XLM-R) pre-trained on 100+ languages
• Fine-tune on limited Somali data
• Arabic transfer (geographic/cultural proximity)
• Afro-Asiatic language family knowledge transfer

Implementation:

# Start with multilingual model
model = AutoModelFor

SequenceClassification.from_pretrained(
    'xlm-roberta-base',  # Pre-trained on 100 languages
    num_labels=3  # Northern, Southern, Central
)

# Fine-tune on Somali data
trainer.train()

2. Data Augmentation

Techniques for Somali:

Back-Translation:

• Somali → English → Somali (introduces variation)
• Use with caution (may introduce artifacts)

Synonym Replacement:

• Replace words with Somali synonyms
• Maintain grammatical structure

Character-Level Noise:

• Add/remove diacritics
• Simulate OCR errors (if data source is scanned)

Example:

# Simple augmentation
def augment_somali_text(text):
    # Preserve meaning, add variation
    return varied_text

3. Semi-Supervised Learning

Approach: Use large unlabeled Somali corpus + small labeled set

Techniques:

• Self-training: Train on labeled → predict on unlabeled → add confident predictions
• Co-training: Train multiple models, use agreement
• Pseudo-labeling: Label unlabeled data with existing model

For This Project:

• Leverage web-scraped Somali text (Wikipedia, news, social media)
• Use dialect classifier to pseudo-label unlabeled text
• Iteratively improve with high-confidence predictions

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Morphological Considerations

Somali Language Characteristics