Low-Resource Language NLP Pipeline

Develop comprehensive NLP pipeline for African languages with <1M speakers addressing the complete language technology stack. Build speech recognition system achieving <20% WER using wav2vec2/Whisper fine-tuning with <100 hours of transcribed audio, implementing data augmentation techniques (speed perturbation, noise addition, synthetic data generation), and cross-lingual transfer from related high-resource languages.

Create neural machine translation system with BLEU score >25 using mBART/M2M-100 as base models, implementing back-translation for data augmentation, handling code-switching between local language and English/French/Arabic, and supporting dialectical variations within the language. Develop text generation capabilities for common use cases (greetings, business communications, educational content) using GPT-2/mT5 fine-tuning with <10k text samples. Implement sustainable data collection infrastructure including mobile crowdsourcing app for native speakers, gamification for data contribution incentives, quality validation pipeline with inter-annotator agreement, and community engagement tools for continuous improvement.

Deploy all models on mobile devices with <500MB total size, <2 second response time, offline functionality, and battery-efficient inference. Create reusable framework and documentation enabling adaptation to other low-resource languages within 1 week of effort.

The Low-Resource Language NLP Pipeline Challenge addresses the critical digital divide facing speakers of minority and indigenous languages, particularly in Africa where hundreds of languages lack basic digital tools. This challenge calls for comprehensive NLP solutions for languages with fewer than 1 million speakers.

Participants will develop end-to-end NLP pipelines encompassing speech recognition (ASR), machine translation, text generation, and deployment on mobile devices. The challenge requires innovative approaches to overcome data scarcity, including techniques for data collection, augmentation, and transfer learning from related languages.

Key components include building speech recognition systems with <20% word error rate despite limited training data, creating bidirectional translation between the target language and major languages (English, French, Arabic, or Portuguese), implementing text generation for basic communication needs, and ensuring all models run efficiently on mobile devices for offline use.

The solution must include sustainable data collection strategies such as crowdsourcing platforms, community engagement tools, and data validation pipelines. Participants should demonstrate creative use of techniques like cross-lingual transfer learning, few-shot learning, unsupervised pretraining, and linguistic rule integration.

Special consideration will be given to solutions that engage native speaker communities, preserve cultural context in translations, handle code-switching and dialectical variations, and create reusable frameworks for other low-resource languages.