Authors: Navjot Singh Dipanjan Chatterjee Jagannath Sahoo

We live in a global world, and language shouldn’t get in the way of connection. Whether it’s growing your business, helping customers, or training teams in different countries—clear communication makes all the difference. That’s where AI translation comes in. It’s helping industries everywhere break language barriers and connect with people in a more meaningful way.

Ecommerce: Consumers are more likely to buy if they understand what they’re getting. Translating product details, reviews, and checkout pages boosts sales of products
Hospitality and Tourism: Translating websites, menus, and signs helps travelers feel welcome and makes their experience smoother and more enjoyable.

Translation Methods

Translation can be carried out through traditional methods or by using AI-powered translation.

Article content — A Comparison between Traditional Translation Vs AI Translation

How AI Translation Works

AI translation uses machine learning and natural language processing (NLP) to make translations more accurate. Here’s a quick breakdown

Data Training: AI learns from huge amounts of multilingual data like books and articles to understand language patterns, grammar, and common phrases.
Context Awareness: Instead of doing word to word translation .AI looks at the entire sentences to understand the context and then do the translation e.g., “bank” as a financial institution or riverside).
Continuous Learning: AI improves over time by learning from user feedback and corrections, constantly getting better.
Customization: Businesses can fine-tune translations with specialized glossaries to keep terms consistent across translations.

Different Ways to translate text using AI translation:

When translating business content, it is not just about converting words. It is more about preserving accuracy, brand tone, and industry-specific meaning. There are a few powerful approaches to help business achieve high-quality, customized translations:

In this blog we will be discussing Azure Based text translation services, LLM based Translation approaches:

Azure AI Translator

Azure AI Translator is Microsoft’s cloud-based service that helps businesses translate text and documents quickly, accurately, and on a scale—no fine-tuning required.

Key Features:

Neural Machine Translation (NMT): Understands context for natural, fluent translations.
100+ Languages: Supports a wide range of global languages and dialects.
Secure & Scalable: Built on Azure’s trusted infrastructure, perfect for enterprise needs.

LLM Based Translation Approach:

LLM-based translation uses large language models like GPT4o, to generate context-aware, fluent translations by understanding meaning, tone, and style.

Advantages of LLM Based Approach:

Few shot learning: Sample examples can be provided inside the prompt which can help LLM to learn patterns and take it into the consideration while doing the translation
Flexibility: Prompts can be fine-tuned easily, and you can include a variety of examples to help the LLM learn translation patterns, control tone, and compare different outputs effectively.

Disadvantages of LLM Based Approach:

Glossary Enforcement: We cannot enforce any custom terminology or vocabulary as the glossary in the LLM based approach
Hallucination Risk: LLMs may occasionally invent or alter facts, introduce content that is not present in the source text, or misinterpret domain-specific terms while doing translation.

Azure AI Custom Translator   :

Azure AI Custom Translator is a feature within the Azure AI Translator service that uses advanced neural machine translation (NMT) technology to create custom translation models. We can provide following datasets for the AI Custom Translator model:

Training Dataset: This is the primary data used to train the model in your specific domain and style. It requires at least 10,000 bilingual sentence pairs for the best results.
Test Dataset: A separate set of bilingual sentence pairs not included in the training data set and will be used to automatically compute the evaluation metrics such as BLEU score to compare the performance.
Dictionary (Glossary) Data: A list of key terms or phrases with their preferred translations, ensuring consistency for brand names, product terms, or specialized language.
Tuning Data: A smaller, high-quality dataset used to fine-tune the model for tricky or nuanced phrases. It helps polish the model for better accuracy.

Important pointers about the datasets:

All datasets (except the training dataset) are required for creating a custom model.
Datasets should be in bilingual pairs (source and target languages).
Supported file formats include .csv, .tsv, .xlsx, .tmx, .xliff, and .txt.

Steps for training the Model:

Step1: Create Project in Azure Custom Translator Portal:

Step2: Upload the Data set

Step3: Train the model

Step4: Training of model succeeded

Step5 Publish the model

After publishing the model, you can use it for translations by calling the Translator API and specifying the model’s category ID.

Advantages of Azure Custom Training Model:

Captures Patterns in Data: Learns and reflects specific patterns such as brand names, product categories, and more.
Understands Tone & Nuance: Adapts to the tone of your content — formal, conversational, technical, etc. while taking care of the translation

Drawbacks of Azure AI custom translator:

Requires High-Quality Data: Custom models rely heavily on well-aligned, clean bilingual data. Providing poor or noisy data can lead to worse performance than the baseline model.
Time-Consuming Setup: Preparing, aligning, and formatting training/test/tuning datasets can be labor-intensive. Training the custom training model takes lot of time. To train a model at least 10k records are required. Model training can take anywhere between 10hr -16hrs depending on the data volume.
Limited Real-Time Adaptability: Model doesn’t learn dynamically — you need to retrain or update the model to improve it.

Custom Approach (Custom Azure AI + LLM approach)  :

We can also combine both Azure Custom Translator and LLM-based approaches to improve translation quality and meet specific business requirements. One such use case is detailed below:

Product Listing Titles (Character Limit Control – E-commerce/Retail):

Scenario: An online marketplace—such as one selling apparel or electronics—needs to translate product titles for different regions, with a strict requirement that each title must not exceed 100 characters.
Challenge: Azure Custom Translator may occasionally generate translations that exceed the character limit due to language expansion or added details.
Solution: In such cases, the translated output can be passed to an LLM with clear instructions to shorten the text without altering its core meaning, by intelligently removing or rephrasing less important information to meet the character limit.

Handle and streamline PII and PCI data for Text Translation :

Why Use PII Checker in Text Translation?

Compliance: Ensures documents comply with GDPR, HIPAA, and other data privacy regulations.
Security: Prevents leakage of sensitive data during translation or external processing.
Automation: Enables scalable and automated redaction or anonymization before translation.
Customization: Microsoft Presidio supports custom recognizers for business-specific sensitive data.

How It Works:

Detect PII: Run Microsoft Presidio Analyzer to identify PII entities in the input text.
Anonymize/Redact: Mask or remove PII using Presidio Anonymizer.
Translate: Send cleaned text for translation (e.g., using Azure Translator).
Reconstruct: Optionally reinsert redacted PII or reformat document for delivery

Sample Output:

Note: Before translation, the input text should first go through a PII service to detect or redact sensitive data, then be sent to the translation service.

Evaluation of AI Translated output :

In the domain of AI translation, the evaluation of machine-generated translations is of paramount importance. Various metrics have been developed to assess the accuracy and fluency of these translations.

BLEU Score: It is a precision-oriented metric designed to quantify the degree of correspondence between a machine-generated translation and one or more reference translations.
METEOR Score: It is developed to address certain limitations of the BLEU score, such as its insensitivity to word order and its inability to handle synonyms and paraphrases effectively. METEOR incorporates both precision and recall, as well as additional features such as stemming and synonymy.
COMET Score: The COMET score represents a contemporary metric that utilizes pre-trained language models to provide a comprehensive evaluation of translation quality. It aims to address the shortcomings of both BLEU and METEOR scores by considering semantic similarity and contextual appropriateness.

Conclusion :

In today’s multilingual world, it’s important to understand how AI translation works. This article explained the key parts, methods, and challenges behind AI translation systems. It gives you the knowledge to build translation tools, add language support to apps, and improve speed and accuracy—helping you make smart choices from the start.

References:

https://github.com/Unbabel/COMET

https://azure.microsoft.com/en-us/products/ai-services/ai-translator

https://learn.microsoft.com/en-us/azure/ai-services/translator/custom-translator/overview

https://learn.microsoft.com/en-us/azure/ai-services/translator/

https://arxiv.org/pdf/2302.14520

https://microsoft.github.io/presidio/

https://huggingface.co/spaces/evaluate-metric/bleu