Adaptive Translation Engine for Low Resource Language

This proposal outlines an advanced phase of development for a multilingual translation and text analysis system, specifically designed for low-resource languages. The system targets the immense linguistic diversity found in the Indo-Pacific region, where language and dialect variation is extremely dense. Phase I of the project, conducted with the University of Southern California's Information Sciences Institute (ISI), successfully demonstrated a proof of concept (PoC) for an end-to-end training system based on Massively Multilingual Neural Machine Translation (NMT). This system is capable of translating over six hundred languages to English and has shown promising results in translating and fine-tuning for specific low-resource languages such as Tok Pisin and Buginese. The proposed Phase II project seeks to build upon this foundation by improving translation accuracy, user interface, infrastructure, and developing methods for large-scale text analysis and classification directly in low-resource languages. Specific technical objectives include implementing advanced translation evaluation methods, improving baseline translation models, and developing new capabilities for rapid domain adaptation and text classification. These improvements aim to produce a robust, scalable system that integrates seamlessly with existing Army technologies, providing significant advancements in communication, information gathering, and analysis during military and civilian operations. The anticipated outcome is a fully-productized system ready for DoD integration, enabling efficient, accurate communication and analysis across a broad spectrum of linguistic settings. This technology promises to enhance operational effectiveness in diverse scenarios, from peacekeeping and crisis management to intelligence gathering and local media monitoring. With demonstrated interest from government stakeholders and a proven track record in transitioning SBIR research to commercial applications, this project represents a critical step forward in the application of AI and NLP technologies to address the challenges of linguistic diversity in strategic regions.