Code-Switching: Processing Mixed-Language Text

Module: Natural Language Processing | Difficulty: Advanced

Code-Switching Types

• Intra-sentential: Within a sentence
• Inter-sentential: Between sentences
• Tag-switching: Single word insertions

Language Identification

Challenges

1. Limited annotated data
2. Language identification errors
3. Morphological complexity

import torch
import torch.nn as nn

class CodeSwitchingModel(nn.Module):
    def __init__(self, bert_model, n_languages):
        super().__init__()
        self.bert = bert_model
        self.language_tagger = nn.Linear(768, n_languages)
    def forward(self, input_ids, attention_mask):
        outputs = self.bert(input_ids, attention_mask=attention_mask)
        hidden = outputs.last_hidden_state
        return self.language_tagger(hidden)

Research Insight: Code-switching is common in multilingual communities but understudied in NLP. The key challenge is that code-switching follows sociolinguistic patterns that are hard to model. Multilingual pre-training improves code-switching performance by 15-20%.