English-Polish Shopping Domain Parallel Corpora

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

About This OTS Dataset

Introduction

The English-Polish Shopping Parallel Corpora is a high-quality bilingual dataset designed for developing multilingual language models, machine translation engines, and NLP systems in the Shopping and E-Commerce domain. With over 50,000 professionally translated sentence pairs, this dataset captures the linguistic diversity and domain-specific expressions commonly found across online retail platforms.

Dataset Content

•Volume and Translator Diversity

•Sentence Pairs: 50,000+

•Contributors: Over 200 native and professional translators

•Content Source: Original content developed exclusively for language model training and localization purposes

•Sentence Diversity

•Sentence Length: 7 to 25 words

•Sentence Structure: Simple, compound, and complex sentences

•Forms Included: Interrogative, imperative, affirmative, and negative

•Voice: Active and passive constructions

•Figurative Language: Includes idioms, metaphors, and domain-specific expressions

•Discourse Markers: Rich use of logical connectors, transitions, and conjunctions

•Bidirectional Translation: Includes both English to Polish and Polish to English translations

Domain-Specific Focus

•Shopping Industry Terminology

•Covers e-commerce workflows, product specs, checkout and payment flows, customer service language, and return policies

•Includes industry expressions, colloquialisms, and user-generated content language such as reviews and FAQs

•Rich representation of subdomains such as electronics, fashion, beauty, and lifestyle

•Contextual Coverage

•Product descriptions and specifications

•Customer reviews and star ratings

•Order confirmations and payment messages

•Promotions, ads, discounts, and email marketing copy

•Navigation labels, category blurbs, and app interface strings

•Return and exchange policies

•Customer support interactions, chatbot content, and FAQs

Format and Structure

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Default Format: Excel

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Available Conversions: JSON, TMX, XML, XLIFF, XLS, and other industry-standard localization formats

•Dataset Structure:

•Serial Number

•Unique Sentence ID

•Source Sentence + Word Count

•Target Sentence + Word Count

Usage and Applications

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Machine Translation: Build accurate translation engines for product content, marketing copy, and e-commerce interfaces

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Language Modeling: Train LLMs to understand and generate shopping-specific content

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NLP Tools: Support predictive typing, spell checkers, grammar correction, and text summarization

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Chatbot and Virtual Assistant Training: Enable automated customer support systems in retail environments

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Sentiment and Intent Modeling: Analyze customer tone in reviews, feedback, and transactional queries

Alignment Confidence and Quality Assurance

•All sentence pairs are manually verified by native translators for semantic accuracy, cultural relevance, and natural fluency

•Quality assurance includes multi-stage review, stylistic alignment, and syntactic consistency checks

•Each sentence pair is aligned and validated for use in supervised MT or retrieval-based NLP tasks

Tokenization and Preprocessing

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Default Version: Delivered in raw, untokenized format

•Optional Preprocessing Available Upon Request:

•Tokenization

•Lowercasing

•POS tagging

•Named entity masking

•Sentence-type classification (e.g., declarative, question, command)

Secure and Ethical Collection

•Created using FutureBeeAI’s secure proprietary platform, Yugo

•Dataset remained within a closed, secure environment during creation and storage

•No personally identifiable information (PII) is included

•All content is original and free of third-party copyrights or licensing restrictions

Updates and Customization

•Regularly updated with new sentence pairs, subdomains, and lexical variations

•Custom collection available in any domain or language pair

•Annotation Services Available:

•Named Entity Recognition (NER)

•Sentiment and intent labeling

•POS tagging

•Multiple translation variants

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Sentence Classification Available: Tag by category, sentence type, or usage scenario

Licensing

This dataset is developed and maintained by FutureBeeAI and is available for commercial use. Licensing is flexible and can be tailored to enterprise, academic, or startup needs.