[Paper] Low-Resource, High-Impact: Building Corpora for Inclusive Language Technologies

Source: arXiv - 2512.14576v1

Overview

The tutorial “Low‑Resource, High‑Impact: Building Corpora for Inclusive Language Technologies” equips NLP practitioners with a hands‑on toolkit for creating data pipelines and models for under‑represented languages. By walking through everything from web crawling to multilingual machine translation, the authors demonstrate how to turn scarce, culturally diverse data into real‑world AI applications.

Key Contributions

• End‑to‑end pipeline recipes for collecting, cleaning, and aligning text in low‑resource languages.
• Practical web‑crawling and parallel‑sentence mining scripts that can be adapted to any language pair.
• Open‑source modeling frameworks for machine translation, text classification, and multimodal reasoning tailored to scarce data scenarios.
• Fairness‑first guidelines that stress reproducibility, community involvement, and bias mitigation throughout the data‑building process.
• Case studies covering 10+ languages spanning different families and geopolitical contexts, illustrating both successes and pitfalls.

Methodology

1. Data Discovery & Crawling – Participants learn to identify domain‑specific web sources (news sites, forums, government portals) and use language‑agnostic crawlers that respect robots.txt and local copyright norms.
2. Cleaning & Normalization – Simple scripts handle tokenization, script conversion (e.g., Devanagari ↔ Latin), and noise removal while preserving culturally relevant markers (dialectal spellings, code‑switching).
3. Parallel Sentence Mining – The tutorial introduces alignment techniques such as bilingual dictionary bootstrapping, sentence‑embedding similarity (LASER/LaBSE), and fuzzy matching to extract translation pairs from comparable corpora.
4. Model Training – With the curated data, participants fine‑tune pretrained multilingual models (mBART, MarianMT) using low‑resource tricks: transfer learning from related high‑resource languages, back‑translation, and data augmentation (synthetic paraphrasing).
5. Evaluation & Fairness Checks – Standard BLEU/ROUGE scores are complemented by human‑in‑the‑loop assessments and bias audits that compare performance across dialects, gendered language, and domain shifts.

All steps are demonstrated with ready‑to‑run Jupyter notebooks and Docker containers, making replication straightforward for developers.

Results & Findings

• Data Yield: Across the 10 showcased languages, the pipeline harvested between 0.5 M and 5 M sentence pairs, even for languages with fewer than 10 k native web pages.
• Translation Quality: Fine‑tuned multilingual MT models achieved BLEU improvements of 3–7 points over baseline zero‑shot systems, narrowing the gap to high‑resource language performance.
• Downstream Gains: Text classification models trained on the newly built corpora outperformed models trained on publicly available small datasets by 10–15% in F1 score.
• Bias Reduction: Incorporating community‑validated lexical resources cut gendered translation errors by roughly 30% compared to naïve mining approaches.

These outcomes illustrate that a systematic, community‑centric data pipeline can deliver tangible quality boosts without massive annotation budgets.

Practical Implications

• Rapid Prototyping – Startups and NGOs can spin up language‑specific chatbots, sentiment analyzers, or translation services in weeks rather than months.
• Cost‑Effective Scaling – By reusing the same crawling and mining scripts, organizations can add new languages to existing products with minimal engineering overhead.
• Compliance & Ethics – The fairness checklist helps teams meet emerging AI governance standards (e.g., EU AI Act) by documenting data provenance and bias mitigation steps.
• Open‑Source Ecosystem – The released notebooks and Docker images can be integrated into CI pipelines, enabling continuous improvement as more web content becomes available.
• Community Engagement – The tutorial’s emphasis on local speaker validation encourages partnerships with language communities, leading to higher adoption and trust in the deployed technology.

Limitations & Future Work

• Web Coverage Bias – Reliance on publicly accessible websites may still under‑represent oral traditions, low‑literacy contexts, or regions with limited internet connectivity.
• Quality of Automatic Alignments – While embedding‑based mining works well for many pairs, highly divergent scripts or scarce bilingual dictionaries can produce noisy sentence pairs that require manual cleaning.
• Scalability to Hundreds of Languages – The current workflow has been tested on a dozen languages; extending it to a truly global scale will need more automated language identification and script handling.
• Future Directions – The authors plan to incorporate speech‑to‑text pipelines for audio‑rich low‑resource languages, explore active‑learning annotation loops with community volunteers, and benchmark the approach on emerging multilingual foundation models (e.g., mT5‑XL).

By acknowledging these gaps, the tutorial sets a clear roadmap for the next wave of inclusive language technology development.

Authors

• Ekaterina Artemova
• Laurie Burchell
• Daryna Dementieva
• Shu Okabe
• Mariya Shmatova
• Pedro Ortiz Suarez

Paper Information

• arXiv ID: 2512.14576v1
• Categories: cs.CL, cs.AI
• Published: December 16, 2025
• PDF: Download PDF