Train a Word-Alignment Model for Low-Resource Catalan-Aranese
Overview
What this challenge is about.
Train a Word-Alignment Model for Low-Resource Catalan-Aranese. Intermediate challenge in code. Writing production code that solves real engineering problems,...
The Brief
What you'll do, and what you'll demonstrate.
Pick the best word-alignment model for a low-resource Catalan-Aranese language pair under a public-procurement constraint set.
This is not a coding exercise. It is the work a software engineer does between a Jira ticket and a merged PR. That distinction matters to every hiring manager who has seen candidates solve LeetCode problems and none who have shipped production code under real constraints.
When you finish, you will have something most graduates do not: a real-world deliverable, verified by Ewance, that you can show to a hiring manager and say "I did this. Here is the proof."
Earning criteria — what you'll demonstrate
- Train and evaluate statistical and neural word alignment models
- Apply Alignment Error Rate and related metrics correctly
- Reason about low-resource language constraints and tooling availability
- Communicate trade-offs to a public-sector procurement audience
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Alignment
Apply alignment to solve real industry problems and demonstrate production-level capability.
- Neural Mt
Apply neural mt to solve real industry problems and demonstrate production-level capability.
- Low Resource Mt
Apply low resource mt to solve real industry problems and demonstrate production-level capability.
- Model Evaluation
Apply model evaluation to solve real industry problems and demonstrate production-level capability.
- Transformer
Apply transformer to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
NLP Engineer
Word-alignment work on low-resource pairs is exactly the niche NLP engineers own at govtech and translation-tooling shops.
This challenge sharpens
- alignment
- low-resource-mt
- transformer
Applied AI Scientist
Picking a model that fits public-sector procurement constraints (open-source, supportability) is core applied-AI work at consultancies serving governments.
This challenge sharpens
- model-evaluation
- low-resource-mt
- alignment
ML Researcher
Comparing statistical and neural alignment with rigorous AER reporting is the kind of focused ML-research deliverable that small NLP labs hire for.
This challenge sharpens
- neural-mt
- alignment
- model-evaluation