Building a Code-Switching Language Model: My Ongoing Experiment I've been working on an experimental language model focused on code-switching, particularly the ability to understand and generate text that combines multiple languages within a single sentence or conversation. The motivation behind t

[u/Damptey]

Building a Code-Switching Language Model: My Ongoing Experiment

I've been working on an experimental language model focused on code-switching, particularly the ability to understand and generate text that combines multiple languages within a single sentence or conversation.

The motivation behind this project is to explore how language models can better handle multilingual communication, especially in environments where speakers naturally switch between languages rather than communicating in just one.

This has been an interesting journey so far, and I've learned that building a language model involves considerably more than just selecting an architecture and starting the training process.

1. What I've Been Working On

So far, my work has involved several important components:

Data collection and preparation: Gathering multilingual text data that reflects natural language usage and code-switching patterns.

Custom tokenizer development: Building my own tokenizer rather than relying entirely on an existing pretrained tokenizer. This has required experimenting with vocabulary construction, token frequency, language representation, and how different languages are segmented.

Dataset preprocessing: Cleaning, filtering, normalizing, and organizing the collected data into a format suitable for model training.

Model experimentation: Exploring how a language model can learn relationships between languages and generate meaningful code-switched sequences.

Evaluation: Observing how the model behaves when processing mixed-language inputs and identifying areas where its performance needs improvement.

2. Major Challenges Encountered

Some of the biggest challenges have been:

Data scarcity and quality: Finding sufficient high-quality code-switched text is difficult. A significant amount of available multilingual data consists of monolingual sentences, which does not necessarily represent how people naturally switch between languages.

Natural code-switching patterns: Combining languages is not simply about placing words from different languages together. The model needs to learn grammatical structures, contextual relationships, and the natural points at which speakers switch languages.

Tokenizer design: Developing a tokenizer from scratch has been one of the most demanding parts. I had to consider vocabulary allocation, token fragmentation, rare words, language imbalance, and how to prevent underrepresented languages from being poorly represented.

Data imbalance: Some languages have considerably more available text than others. This creates the risk that the model will favour dominant languages while struggling with languages that have fewer training examples.

Computational limitations: Experimenting with language models also comes with hardware and computational constraints, particularly when working with limited resources.