Problem Statement
Accurate language identification is crucial for multilingual systems, yet traditional methods often struggle with computational efficiency and accuracy trade-offs. This challenge explores using bigram analysis—inspired by machine translation evaluation metrics—to build an efficient and accurate language identifier.
The Approach: BLEU-Inspired Language ID
Machine translation evaluation relies heavily on BLEU scores, which measure translation quality by calculating n-gram overlap between outputs and references. This challenge adapts this precision-focused n-gram matching concept for language identification.
Instead of evaluating translations, we:
- Extract word bigrams from input text
- Calculate bigram overlap with pre-computed language models
- Use log probability scoring (similar to BLEU's precision calculations) to identify the most likely language
This approach offers high accuracy by capturing linguistic patterns through sequential word pairs, which are highly discriminative across languages.
Why Bigrams?
Bigrams capture:
- Word order patterns specific to each language
- Common phrase structures (e.g., "of the" in English, "de la" in French)
- Efficient computation compared to longer n-grams
- Proven effectiveness in MT evaluation (BLEU) adapted for classification
Dataset Overview
We provide a pre-processed bigram dataset covering three major languages:
- English (eng)
- French (fra)
- Twi (twi) – a widely spoken language in Ghana
Dataset Structure:
| Column | Description |
|---|
ngram | The word bigram, e.g., "hello world" |
lang | Three-letter language code (eng, fra, twi) |
lang_id | Numeric identifier (1, 2, 3) |
count | Frequency of this bigram in the language |
Dataset Links:
The Challenge
Your task is to:
- Improve Accuracy: Enhance the baseline bigram approach to achieve higher language identification accuracy
- Consider incorporating character-level features
- Experiment with different smoothing techniques
- Optimize probability calculations
- Optimize Efficiency: Scale the solution for production use
- Process single sentences with minimal latency
- Enable batch processing of large corpora (target: 100K+ sentences)
- Minimize memory footprint and computation time
- Validation: Test your approach against a held-out validation set (provided at hackathon start)
Key Goals & Deliverables
- Accurate Single and Multiple Sentence ID: Accurate language detection for individual and multiple sentence inputs.
- Speed and Efficiency: Novel approaches to improving the bigram-based method to process inputs as fast as possible with efficient use of resources.
Evaluation Criteria
Solutions will be evaluated on:
- Accuracy on the validation set (primary metric)
- Processing speed and efficiency (sentences per second)
Get Started: Review the reference implementation, explore the bigram dataset, and build your optimized language identifier!
