Author: Agung Wibawa, Azhari Azhari, Yunita Sari
Introduction
FDC Stack is a multi-stage optimization framework designed to elevate Neural Machine Translation (NMT) between English and Indonesian. By integrating fine-tuning, knowledge
distillation, and chain-of-thought reasoning, this system delivers high-quality translations that are both contextually accurate and computationally efficient. Whether for technical documentation, educational tools, or mobile applications, FDC Stack bridges linguistic complexity with intelligent design.
The Challenge
Translating between English and Indonesian presents unique linguistic hurdles. The two languages differ significantly in morphosyntactic structure, making direct translation prone to errors in grammar and meaning. Compounding this issue is the scarcity of high-quality parallel corpora, especially for specialized domains like information technology. Standard NMT models often fail to maintain consistent terminology and struggle with producing fluent, coherent translations across
longer texts. These limitations highlight the need for a smarter, more adaptive translation framework.
Our Goal
FDC Stack aims to address these challenges by building a translation system that is both powerful and practical. The framework is designed to adapt to domain-specific language, compress model size for deployment in low-resource environments, and enhance logical flow in translated output.
By simulating human-like reasoning and leveraging structured terminology, FDC Stack ensures that translations are not only accurate but also contextually meaningful and easy to understand.
Methodology
At the heart of FDC Stack is a three-pronged approach. First, the system uses fine-tuning to adapt the XLM-R multilingual Transformer model with the Microsoft Term Collection—a structured dataset of IT terminology. This ensures consistent and precise use of technical language. Second,
knowledge distillation compresses the model, transferring knowledge from a larger teacher model to a smaller student model without sacrificing semantic depth. This makes the system suitable for devices with limited computational resources. Finally, chain-of-thought reasoning is incorporated
during decoding, allowing the model to simulate structured logical thinking and produce more coherent translations at the discourse level.
The decoder is augmented with chain-of-thought capabilities, enabling it to process and generate translations with logical structure and contextual awareness. This architecture merges the precision of AI with the nuance of human reasoning, resulting in a system that is both technically advanced
and practically reliable.
Results
Evaluation of FDC Stack using COMET and BLEU metrics demonstrates significant improvements in translation fidelity, fluency, and terminology accuracy compared to baseline
models. The system consistently produces translations that align with domain-specific expectations and maintain logical coherence across sentences. Real-world testing shows that FDC Stack is capable of handling complex linguistic structures and delivering high-quality output in both academic and applied settings.
Value Proposition
FDC Stack offers a transformative solution for domain-specific translation needs. Its ability to roduce accurate, fluent, and logically structured translations makes it ideal for localization in technology sectors, educational platforms, and mobile applications.
The framework supports Indonesian learners by providing clearer, more reliable translations and empowers developers with a lightweight, deployable model. By combining deep learning with structured reasoning, FDC Stack bridges the gap between language and technology—bringing scalable impact to real-world communication.
