Can AI Translate Technical Content into Indian Languages? Exploring Amazon Translate (English Marathi & Hindi)

Source: Dev.to

Introduction

I’m Vasil, a DevOps Engineer with a passion for building reliable, scalable, and well‑architected cloud platforms. With hands‑on experience across cloud infrastructure, CI/CD, observability, and platform engineering, I enjoy turning complex operational challenges into clean, automated solutions.

I’ve been working with AWS Cloud for over 5 years, and I believe it’s high time I start exploring AI on AWS more deeply. Through these posts, I plan to share practical learnings, real‑world experiences, and honest perspectives from my journey in DevOps, Cloud, and now AI.

Without further delay — let’s dive in 🚀

Why I’m Trying Amazon Translate

As someone who writes about AWS in English, I wanted to explore whether Amazon Translate could help make technical AWS content accessible to regional‑language audiences.

Instead of assuming it would “just work,” I approached this as an experiment:

1. Can Amazon Translate handle technical paragraphs?
2. How does it perform for regional Indian languages like Marathi?
3. How does that compare with a more widely supported language like Hindi?

This post documents what actually happens when you try this in practice — including the limitations.

Architecture Diagram (Simplified)

+-------------------+        +-------------------+        +-------------------+
|   Author writes   |  -->   |   Amazon S3       |  -->   |   Amazon Translate|
|   English article |        | (original file)   |        |   (Marathi)       |
+-------------------+        +-------------------+        +-------------------+
                                                               |
                                                               v
                                                      +-------------------+
                                                      |   Amazon S3       |
                                                      | (translated file) |
                                                      +-------------------+

• An author writes the original article in English and stores it as a text or markdown file in Amazon S3.
• The content is then passed to Amazon Translate, which converts it into Marathi.
• The translated output is stored back in S3 and can be published to platforms like Medium, dev.to, or internal documentation portals.

Note: In many production setups a Lambda function is used to automate this workflow (e.g., trigger translation when a new file is uploaded). In this article we keep things simple and interact with Amazon Translate directly via the AWS CLI, without adding Lambda or other automation.

Prerequisites

• An AWS account
• AWS CLI configured locally (I’ll be using CloudShell)
• Basic familiarity with AWS services

Test Paragraph

What is Amazon Translate? Amazon Translate lets you localize content for diverse global users and translate and analyze large volumes of text to activate cross‑lingual communication between users. Amazon Translate is a neural machine translation service that delivers fast, high‑quality, affordable, and customizable language translation.

Translation to Marathi (mr)

aws translate translate-text \
  --region us-east-1 \
  --source-language-code en \
  --target-language-code mr \
  --text "What is Amazon Translate? Amazon Translate lets you localize content for diverse global users and translate and analyze large volumes of text to activate cross-lingual communication between users. Amazon Translate is a neural machine translation service that delivers fast, high-quality, affordable, and customizable language translation."

Result

Amazon भाषांतर म्हणजे काय? Amazon Translate आपल्याला विविध जागतिक वापरकर्त्यांसाठी सामग्री स्थानिकीकरण करण्यास आणि वापरकर्त्यांमधील क्रॉस-भाषिक संप्रेषण सक्रिय करण्यासाठी मोठ्या Amazon Translate ही एक न्यूरल मशीन भाषांतर सेवा आहे जी जलद, उच्च-गुणवत्तेची, परवडणारी

Observations

• The paragraph is truncated.
• Sentences merge abruptly.
• The translation cuts off before completing the final thought.
• Technical flow and readability suffer.
• The output cannot be used directly (or even fed to an LLM for fine‑tuning) because it is incomplete and lacks proper flow.
• Heavy human intervention would be required to make it publishable.

Translation to Hindi (hi)

aws translate translate-text \
  --region us-east-1 \
  --source-language-code en \
  --target-language-code hi \
  --text "What is Amazon Translate? Amazon Translate lets you localize content for diverse global users and translate and analyze large volumes of text to activate cross-lingual communication between users. Amazon Translate is a neural machine translation service that delivers fast, high-quality, affordable, and customizable language translation."

Result

Amazon Translate क्या है? Amazon Translate से आप विभिन्न वैश्विक उपयोगकर्ताओं के लिए सामग्री का स्थानीयकरण कर सकते हैं और उपयोगकर्ताओं के बीच अंतर-भाषी संचार को सक्रिय करने के लिए बड़ी मात्रा में टेक्स्ट का अनुवाद और विश्लेषण कर सकते हैं। Amazon Translate एक न्यूरल मशीन अनुवाद सेवा है जो तेज़, उच्च-गुणवत्ता, किफायती और अनुकूलन योग्य भाषा अनुवाद प्रदान करती है।

Observations

• Complete paragraph – no truncation.
• Proper sentence boundaries and natural flow.
• Technically accurate and publishable with minimal human editing.

Why the Difference?

It’s important to call out that what we’re seeing here is not unique to Amazon Translate. High‑quality translation for Indian regional languages has always been a hard problem, even outside AWS and cloud services. The challenge appears across traditional NLP systems and modern generative AI models alike.

Contributing Factors

• Linguistic complexity – Languages like Marathi have rich morphology, flexible sentence structures, and context‑heavy grammar. Direct sentence‑to‑sentence mapping from English often loses meaning or flow.
• Limited high‑quality training data – Compared to English or Hindi, regional languages have significantly fewer large, clean, technical corpora available for training translation models.
• Technical vocabulary mismatch – Cloud and software terminology often has no commonly accepted regional equivalent. Models must decide whether to transliterate, translate, or drop the term entirely, which can lead to broken sentences.
• Mixed‑language expectations – In real‑world Indian technical writing, English service names are frequently mixed with regional explanations. Handling this hybrid style consistently remains difficult for automated systems.

PLEASE NOTE!

As I mentioned earlier, the observed shortcomings are not a failure of Amazon Translate; they reflect the broader state of regional language translation today.

Takeaways

1. Amazon Translate works well for Hindi, delivering complete, readable output for technical content.
2. Marathi (and many other regional languages) still suffer from truncation and loss of flow, requiring substantial post‑editing.
3. When building a production pipeline for multilingual documentation, plan for human review—especially for regional languages with limited training data.
4. Keep an eye on future model improvements and community‑driven datasets that may close the quality gap for Indian regional languages.

Happy translating! 🚀

The Broader State of Machine Translation for Indian Regional Languages Today

Amazon Translate does support Marathi (along with several other Indian regional languages), but for long technical paragraphs the output can be unreliable.
Hindi performs significantly better for the same technical content.

Common Work‑arounds

• Breaking content into multiple smaller calls
  • Inefficient
  • Not scalable
  • Still doesn’t guarantee quality

Important: Know these limitations before committing to a regional‑language publishing workflow.

Practical Takeaway

A Realistic Approach Today

1. Use Amazon Translate for exploration and draft creation.
2. Rely on human review and editing for regional languages.
3. Avoid assuming parity across all supported languages.

This experiment wasn’t about proving that Amazon Translate is perfect — it’s about understanding where it works well and where it still struggles.

Bottom‑Line Takeaway

• Amazon Translate is strong for widely used languages.
• Regional technical localization is still a work in progress.

And that’s okay — knowing the limits is just as valuable as knowing the features.