English-Gujarati Medical Domain Parallel Corpora

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

Category

Parallel Corpora

Volume

50K+ Sentence Pairs

Last Updated

July 2025

Number of participants

200+ People

Comparable parallel corpora in Medical domain in Gujarati
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About This OTS Dataset

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Introduction

The English-Gujarati 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 Gujarati 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-Gujarati and Gujarati-to-English sentence sets to enhance model performance in both directions
  • •
    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

  • •
    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

  • •
    Medical Machine Translation: Build domain-accurate translation engines for clinical, pharmaceutical, and health-related content
  • •
    NLP Research and Tools: Train tools like grammar checkers, spell correction systems, and summarization engines tailored to medical texts
  • •
    Large Language Model (LLM) Training: Fine-tune foundational models for high-stakes use cases such as AI-assisted diagnosis or clinical data interpretation
  • •
    Conversational AI: Train medical chatbots and virtual health assistants to understand complex clinical conversations
  • •
    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

  • •
    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

  • •
    Collection Platform: Built using FutureBeeAI’s proprietary data platform, Yugo
  • •
    Data Privacy: No personally identifiable information (PII) is included
  • •
    Security Standards: Data remained within a secure and controlled environment throughout collection and translation
  • •
    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.

    Use Cases

    Use of parallel corpus dataset in MT Engine

    MT Engine

    Use of parallel corpus dataset in Language modeling

    Language model

    Use of parallel corpus dataset in Predictive keyboards

    Predictive keyboards

    Use of parallel corpora dataset in Spell checker

    Spell check

    Use of parallel corpus dataset in grammar correction tool

    Grammar correction

    Use of parallel corpus dataset in Text/speech system

    Text/speech systems

    Dataset Sample(s)

    Card Head Line

    SAMPLE

    SOURCE LANGUAGE
    TARGET LANGUAGE
    Smoking and drinking alcohol is injurious to health.
    ધૂમ્રપાન અને દારૂ પીવું સ્વાસ્થ્ય માટે હાનિકારક છે.
    The organs of two brain dead patients were donated on the same day in Surat.
    સુરતમાં એક જ દિવસે બે બ્રેનડેડ દર્દીના અંગોનું દાન કરવામાં આવ્યું.
    The patient underwent a heart transplant at a hospital 273 km in 90 minutes Far away from Ahmedabad .
    90 મિનિટમાં 273 કિ.મી. દૂર અમદાવાદની હોસ્પિટલમાં દર્દીનું હાર્ટ ટ્રાન્સપ્લાન્ટ કરાયું.
    Swine flu became more deadly than Corona.
    કોરોના કરતાં પણ સ્વાઇન ફ્લૂ વધુ ઘાતક બન્યો.
    The highest number of swine flu cases were reported this year.
    આ વર્ષે સ્વાઇન ફ્લૂના સૌથી વધુ કેસ નોધાયા.
    Gujarat ranks second in the highest number of deaths due to swine flu.
    સ્વાઇન ફ્લૂથી સૌથી વધુ મૃત્યુમાં ગુજરાત બીજા સ્થાને.
    Gujarat reported 1315 cases of swine flu in a month out of which 34 died.
    ગુજરાતમાં એક મહિનામાં સ્વાઇન ફ્લૂના ૧૩૧૫ કેસ, જેમાંથી ૩૪ નું મૃત્યુ થયું.
    Alzheimer's disease, which cripples the elderly even though the body is healthy.
    શરીરે સ્વસ્થ હોવા છતાં વૃદ્ધોને પાંગળા બનાવી દેતી બીમારી, અલ્ઝાઈમર.
    The number of people suffering from Alzheimer's in India is around 3.5 million.
    ભારતમાં અલ્ઝાઈમરથી ૫ીડાતા લોકોની સંખ્યા ૩૫ લાખ જેટલી છે.
    More than two and a half crore people in world suffer from the Sourceoblem of amnesia.
    દૂનિયામાં અઢી કરોડથી પણ વધુ લોકો સ્મૃતિભ્રંશની સમસ્યા ભોગવે છે.

    ATTRIBUTES

    Target Language :Gujarati
    Source Language :English
    Domain :Medical

    Dataset Details

    Card Head Line

    Dataset Type

    Text Corpus

    Volume

    50K+ Sentences

    Media type

    Text

    Language Pair

    English-Gujarati

    File Details

    Card Head Line

    Type

    Bilingual

    Word Count

    7 to 25 Words per Asset

    Format

    XLSX, TMX, XML, XLIFF, XLS

    Annotation

    NA

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