English-Urdu Medical Domain Parallel Corpora

A high-quality bilingual dataset containing sentence-aligned English-Urdu text pairs for the Medical domain. Supports translation, NLP, and LLM training.

About This OTS Dataset

Introduction

The English-Urdu Medical Parallel Corpus is a professionally curated bilingual dataset designed to support the development of language models, translation systems, and NLP applications in the healthcare and medical sectors. With over 50,000 sentence pairs covering a wide range of medical topics, this dataset serves as a powerful resource for improving multilingual AI systems in one of the most critical domains like healthcare.

Dataset Content

•Volume and Translator Diversity

•Sentence Count: 50,000+ parallel sentences

•Translator Base: Contributions from over 200 native Urdu translators with subject matter familiarity

•Data Origin: All content is purpose-built and translation-ready, developed specifically for machine learning applications

•Sentence Diversity

•Length Range: Sentences range from 7 to 25 words

•Structural Variety: Includes simple, compound, and complex sentence structures

•Form Types: Covers questions, commands, affirmations, and negations

•Voice: Balanced inclusion of both active and passive constructions

•Bi-directional Translation: Includes both English-to-Urdu and Urdu-to-English sentence sets to enhance model performance in both directions

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Linguistic Features: Domain-relevant metaphors, idioms, and phrases

•Logical flow supported by a rich use of discourse markers and connectors

Medical Domain Specifics

•Terminology Coverage

The dataset reflects real-world terminology from across the medical field, including:

•Anatomy and physiology

•Diseases and symptoms

•Diagnosis and treatment protocols

•Pharmaceutical and drug-related terminology

•Medical devices, procedures, and administrative documentation

•Real-World Contexts

This corpus features data drawn from various healthcare settings and content types such as:

•Patient-doctor dialogues and telehealth interactions

•Diagnosis summaries and treatment plans

•Clinical notes and discharge instructions

•Medical research abstracts and journal-style excerpts

•Drug descriptions, usage guidelines, and safety instructions

•Hospital policy and consent-related materials

•Informational content around wellness, supplements, and preventive care

•Cross-Domain Elements

In addition to core medical language, the dataset also includes related content from:

•Healthtech and medical devices

•Wellness and self-care

•Nutrition and lifestyle medicine

Format and Structure

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Available Formats: Delivered in Excel, with optional conversions to JSON, TMX, XML, XLIFF, or other localization-ready formats

•Fields Included:

•Serial Number

•Unique ID

•Source Sentence

•Source Word Count

•Target Sentence

•Target Word Count

Applications and Use Cases

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Medical Machine Translation: Build domain-accurate translation engines for clinical, pharmaceutical, and health-related content

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NLP Research and Tools: Train tools like grammar checkers, spell correction systems, and summarization engines tailored to medical texts

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Large Language Model (LLM) Training: Fine-tune foundational models for high-stakes use cases such as AI-assisted diagnosis or clinical data interpretation

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Conversational AI: Train medical chatbots and virtual health assistants to understand complex clinical conversations

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Terminology Alignment and Glossary Expansion: Extend multilingual terminologies with real-world, context-sensitive examples

Alignment Confidence and Quality Assurance

Each sentence pair has been manually reviewed to ensure high semantic fidelity and natural fluency in both languages.

•Alignment Type: One-to-one sentence-level alignment

•Verification: Manual validation for accuracy, consistency, and tone by bilingual experts

•Fluency Checks: All translations are reviewed for naturalness, contextual correctness, and domain appropriateness

Tokenization and Preprocessing

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Default Format: Delivered in raw, untokenized format for maximum flexibility

•Optional Preprocessing:

•Tokenization

•Lowercasing

•Part-of-speech tagging

•Named entity masking

•Sentence-type classification (e.g., imperative, interrogative, declarative)

•Subdomain labeling (e.g., cardiology, pediatrics, mental health)

Secure and Ethical Collection

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Collection Platform: Built using FutureBeeAI’s proprietary data platform, Yugo

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Data Privacy: No personally identifiable information (PII) is included

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Security Standards: Data remained within a secure and controlled environment throughout collection and translation

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Licensing Assurance: All content is original and free from third-party copyright claims

Updates and Customization

To meet the evolving needs of AI builders and medical researchers, the dataset is continuously expanded and updated.

•Customizable Options Available:

•Sentence-level annotations (e.g., NER, POS, sentiment, intent)

•Subdomain classification (e.g., oncology, surgery, pharmacology)

•Custom collection in specific medical specialties or regional dialects

•Support for additional language pairs

Licensing

This dataset is developed and maintained by FutureBeeAI and is available for commercial use. Custom licensing packages can be arranged for enterprise, research, or regulatory applications.