English-Turkish Legal Domain Parallel Corpora

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

Category

Parallel Corpora

Volume

50K+ Sentence Pairs

Last Updated

July 2025

Number of participants

200+ People

Legal domain Parallel corpus in Turkish

About This OTS Dataset

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Introduction

The English-Turkish Legal Parallel Corpus is a high-quality bilingual dataset designed to support the development of multilingual legal language models, machine translation systems, and text-based AI tools. With over 50,000 carefully translated sentence pairs, this dataset serves as a critical resource for anyone working on cross-lingual legal technology or NLP applications in the legal field.

Dataset Content

  • Volume and Translator Diversity
  • Sentence Count: Over 50,000 bilingual sentence pairs
  • Translator Base: More than 200 native Turkish linguists with domain familiarity contributed to the translation process
  • Dataset Origin: Built from scratch with legal use cases in mind, ensuring domain relevance and application readiness
  • Sentence Variety
  • Length Range: Sentences contain 7 to 25 words
  • Grammatical Structures: Includes simple, compound, and complex sentences
  • Form Types: Covers questions, commands, affirmations, and negations
  • Voice Representation: Balanced use of active and passive sentence constructions
  • Cross Translation: Dataset includes both English-to-Turkish and Turkish-to-English segments to ensure bidirectional support
  • Linguistic Features:
  • Idiomatic expressions and legal jargon
  • Sentence connectors and discourse markers to preserve argument structure and legal reasoning
  • Legal Terminology Coverage
  • This dataset includes terminology across a wide range of legal subdomains such as:

  • Contracts, agreements, and commercial law
  • Criminal and civil litigation
  • Legal procedures, rulings, and statutory interpretation
  • Administrative, constitutional, and regulatory terms
  • Courtroom dialogue, judgments, and legal advisories
  • Contextual Diversity
  • Sentence pairs are drawn from realistic legal content types, including:

  • Legal briefs, affidavits, and memoranda
  • Terms of service and data protection policies
  • Research articles and legal scholarship
  • Standard forms and templates
  • Legislative, policy, and compliance language
  • Cross-Domain Elements
  • To reflect the multidisciplinary nature of legal texts, the dataset also includes content that touches on:

  • Government policy
  • Business and finance
  • Technology, IP, and cybersecurity law
  • Format and Structure

  • Available Formats: Delivered in Excel, with optional conversions to TMX, JSON, XML, XLIFF, or other localization formats
  • Included Fields:
  • Serial Number
  • Unique ID
  • Source Sentence and Word Count
  • Target Sentence and Word Count
  • Use Cases and Applications

  • Legal Machine Translation: Build accurate translation engines for contracts, laws, and compliance documentation
  • Multilingual NLP Tools: Develop legal summarization tools, AI writing assistants, and terminology alignment engines
  • Language Model Training: Fine-tune LLMs for legal use cases, including retrieval-augmented generation, clause analysis, and legal Q&A
  • Cross-border LegalTech: Enable global legal platforms to support Turkish-English clients and documentation with precision
  • Alignment Confidence and Quality Assurance

    Every sentence pair is manually aligned and verified by expert bilingual reviewers.

  • Alignment Type: One-to-one sentence alignment
  • Quality Review: Human QA ensures high semantic fidelity, domain accuracy, and fluency in both languages
  • Consistency Checks: Legal tone, terminology usage, and formality are maintained throughout
  • Tokenization and Preprocessing

  • Delivery Format: Raw, untokenized sentences by default
  • Optional Preprocessing Includes:
  • Sentence segmentation
  • Tokenization
  • POS tagging
  • Named Entity Recognition (NER)
  • Sentence-type labeling (e.g., declarative, interrogative)
  • Domain and subdomain classification
  • Preprocessing options can be customized as per your integration pipeline.

    Secure and Ethical Collection

  • Collection Platform: Entire dataset was created on FutureBeeAI’s secure data platform, Yugo
  • Data Privacy: No PII or sensitive case data is included
  • Security Protocol: Dataset never left our controlled environment
  • IP-Safe: All content is original, with no third-party copyright concerns
  • Update and Customization Options

    The dataset is regularly updated to include more legal subdomains and translation styles. We also support custom solutions:

  • Annotation Support: POS, NER, sentiment, intent, multiple translations, clause labeling
  • Subdomain Customization: e.g., labor law, family law, corporate law
  • Language Pair Flexibility: Custom collection in other languages or dialects available upon request
  • Licensing

    This dataset is developed by FutureBeeAI and is available for commercial use. Licensing packages can be tailored to enterprise, academic, or platform-specific needs.

    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)

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    Dataset Details

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    Dataset Type

    Text Corpus

    Volume

    50K+ Sentences

    Media type

    Text

    Language Pair

    English-Turkish

    File Details

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    Type

    Bilingual

    Word Count

    7 to 25 Words per Asset

    Format

    XLSX, TMX, XML, XLIFF, XLS

    Annotation

    NA

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