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White Paper
White Paper

The future of translation:
‍AI Agents

Enterprise customers demand high-accuracy translation at scale. AI translation often falls short because it lacks:

Contextual understanding

Accurate translation requires domain-specific knowledge for correct terminology.

Reasoning ability

AI needs to discern tone and intent to avoid mistranslations.

Hallucination control

Current models can generate plausible but inaccurate outputs, compromising fidelity to the original text.

“I think AI agentic machine translation has huge potential for improving over traditional neural machine translation."
- Andrew NG, Founder of Deeplearning.ai 

Agentic Translation Infrastructure

The agentic model utilizes multiple AI agents, each specializing in human-like analysis of translation aspects such as context and reasoning, to improve accuracy and maintain the original meaning. It combines the strengths of transformer-based models, SSMs, and RAG.

SSM

Memory Efficiency

SSMs are more memory-efficient than transformers, allowing them to handle longer sequences of text without running into memory constraints. This is particularly beneficial for translating long documents or maintaining context across multiple sentences.

Linear Scaling

The computational complexity of SSMs scales linearly with the input length, making them faster and more efficient for processing long sequences compared to transformers, which have quadratic scaling.

Interpretability

SSMs offer better interpretability compared to transformers, potentially providing insights into the model's decision-making process and allowing for easier debugging and improvement.

RAG

Factual Accuracy

RAG models can access and leverage external knowledge sources, like databases or the internet, to improve the accuracy and relevance of translations. This is particularly useful for translating technical or specialized content that requires domain-specific knowledge.

Reduced Hallucinations

By relying on retrieved information, RAG models are less likely to generate factually incorrect or nonsensical translations, a common issue with purely generative models.  

Adaptability

RAG models can be easily adapted to new domains or languages by providing them with relevant knowledge sources, making them more versatile and flexible than traditional translation models.

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