SBIR/STTR Research Award

Advanced Machine Translation System for Low-Resource Languages with ATAK Integration

Award identifier 1cc80cbfad0efe7abdd7b7ce7d8960b1de771545e216594210a17ac2ad2367b0Awarding agency Department of Defense · Army

Award amount

$1,499,979

Program and phase

SBIR · Phase II

Award year

2025

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Award details

Awarding agency
Department of Defense
Branch
Army
Recipient company
Deep Media, Inc
Proposal award date
2024-12-18
Contract end date
2026-07-10
UEI
C779QDZBJJE7
Solicitation number
23.4
Topic code
A234-021

Research abstract

This Phase II SBIR project aims to revolutionize machine translation for low-resource languages globally. Building on our Phase I success, we propose a scalable translation system that integrates with the Android Team Awareness Kit (ATAK). Our approach combines advanced AI models, innovative data collection, and comprehensive evaluation to overcome low-resource language translation challenges.Our system's core is a hybrid architecture leveraging NLLB-200's broad language coverage, Claude's advanced language understanding, and custom modules for military-specific content. This approach capitalizes on each component's strengths while mitigating weaknesses.To address data scarcity, we'll implement advanced augmentation techniques like back-translation, pivot language approaches, and synthetic data generation. Partnerships with local institutions will ensure a steady stream of high-quality linguistic data. The system incorporates context-aware translation, crucial for military applications where accurate interpretation of intent and nuance is mission-critical. We'll develop specialized military term corpora using resources like Army Field Manual 1.02.1 and implement domain adaptation techniques for military contexts. ATAK integration represents a significant advancement in field communication. Our TAK plugin will provide real-time text and speech translation within ATAK, combining advanced translation with geospatial and situational awareness tools.A continuous learning framework will allow periodic model retraining with field-collected data, ensuring ongoing accuracy and relevance. The 18-month project timeline covers data collection, model development, ATAK integration, edge deployment optimization, and system refinement. We aim for a 30% improvement in translation quality, focusing on military-specific terminology and contexts.By combining elements from rule-based and transfer learning approaches, our system offers superior performance across various low-resource languages. It provides a comprehensive, adaptable solution for the U.S. Army's diverse translation needs, enhancing capabilities in intelligence gathering, local population interaction, and disaster relief operations. Our language-agnostic approach allows rapid adaptation to new languages and domains, meeting evolving Army needs globally. The modular architecture enables easy integration of new techniques and models as the field evolves. Potential applications extend beyond military use to humanitarian aid, diplomacy, education, and commercial sectors operating in linguistically diverse regions. The system's ability to rapidly adapt to new languages and domains makes it valuable for organizations facing diverse linguistic challenges. By addressing critical communication needs in both military and civilian contexts, this technology has the potential to not only enhance operational capabilities for the U.S. Army but also drive innovation in the broader language technology market.

Technology keywords

Not reported

Other awards by this recipient

Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.

Awards with the same topic code

Verified by an exact topic code match. This is not a text-similarity match.

Research funding history

7 SBIR/STTR records share this UEI (2023–2025). Reported award amounts total $4,474,127. Reported amounts are not federal obligations.

Phases: Phase I, Phase II, Phase II – Direct to Phase II

Agencies: Department of Defense

Agency intelligence

Department of Defense

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