An intelligent framework combining deep learning and fuzzy logic for accurate remote language translation

Area of Science:

• Computational Linguistics
• Artificial Intelligence
• Natural Language Processing

Background:

• Transformer-based Neural Machine Translation (NMT) models excel in contextual understanding but lack interpretability and struggle with nuances.
• Real-time, context-sensitive translation is crucial for global communication, telemedicine, and virtual collaboration.
• Existing NMT models face challenges with idiomatic expressions, low-resource languages, and semantic ambiguity.

Purpose of the Study:

• To propose an intelligent hybrid translation framework integrating transformer NMT with fuzzy logic.
• To enhance translation quality, accuracy, and interpretability beyond current NMT capabilities.
• To address limitations in handling idiomatic expressions, low-resource languages, and semantic ambiguity.

Main Methods:

• Developed a hybrid framework with a transformer NMT backbone and a fuzzy logic inference module.
• Incorporated contextual feature extraction, fuzzification, rule-based evaluation, and defuzzification.
• Conducted experiments on benchmark datasets for high-resource and low-resource language pairs.

Main Results:

• The hybrid framework outperformed rule-based and transformer-only models in BLEU, METEOR, and F1-scores.
• Achieved reduced translation errors and improved user-perceived readability.
• Demonstrated sub-second latency for real-time performance on edge and cloud deployments.

Conclusions:

• Integrating symbolic reasoning (fuzzy logic) with deep learning (NMT) creates scalable, interpretable, and accurate translation systems.
• The proposed framework effectively resolves ambiguity and refines idiomatic content.
• This hybrid approach is highly effective for modern multilingual communication environments.