Dialect Identification: Language Variation and NLP

Module: Natural Language Processing | Difficulty: Advanced

Dialect Identification

Code-Switching

Challenges

1. Limited training data
2. Informal writing
3. Code-switching

Results

import torch
import torch.nn as nn

class DialectIdentifier(nn.Module):
    def __init__(self, bert_model, n_dialects):
        super().__init__()
        self.bert = bert_model
        self.classifier = nn.Linear(768, n_dialects)
    def forward(self, input_ids, attention_mask):
        outputs = self.bert(input_ids, attention_mask=attention_mask)
        cls_output = outputs.last_hidden_state[:, 0]
        return self.classifier(cls_output)

Research Insight: Dialect identification is important for equitable NLP systems. Arabic dialects are particularly challenging because they differ from MSA in phonology, morphology, and syntax. Code-switching between dialects and MSA further complicates processing.