English-Tamil Shopping Domain Parallel Corpora

The dataset consists of bilingual sentence-aligned corpora for the Shopping domain from English to Tamil and vice versa.

About This OTS Dataset

Introduction

Welcome to the English-Tamil Bilingual Parallel Corpora dataset for the Shopping domain! This meticulously curated dataset offers a rich collection of bilingual text data, translated between English and Tamil, providing a valuable resource for developing Shopping domain-specific language models and machine translation engines.

Dataset Content

•Volume and Diversity:

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Extensive Dataset: Over 50,000 sentences offering a robust dataset for various applications.

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Translator Diversity: Contributions from more than 200 native translators ensure a wide range of linguistic styles and interpretations.

•Sentence Diversity:

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Word Count: Sentences range from 7 to 25 words, suitable for various computational linguistic applications.

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Syntactic Variety: The corpus encompasses sentences with varying syntactic structures, including simple, compound, and complex sentences.

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Interrogative and Imperative Forms: The corpus includes sentences in interrogative (question) and imperative (command) forms, reflecting the conversational nature of the Shopping industry.

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Affirmative and Negative Statements: Both affirmative and negative statements are represented in the corpus, ensuring different polarities.

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Passive and Active Voice: The corpus features sentences written in both active and passive voice, ensuring different perspectives and representations of information.

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Idiomatic Expressions and Figurative Language: The corpus incorporates idiomatic expressions, metaphors, and figurative language commonly used in the Shopping domain.

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Discourse Markers and Connectives: The corpus includes a wide range of discourse markers and connectives, such as conjunctions, transitional phrases, and logical connectors, which are crucial for capturing the logical flow and coherence of the text.

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Cross Translation: The dataset includes a cross-translation, where a part of the dataset is translated from English to Tamil and another portion is translated from Tamil to English, to improve bi-directional translation capabilities.

Domain Specific Content

This Parallel Corpus is meticulously curated to capture the linguistic intricacies and domain-specific nuances inherent to the Shopping industry.

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Industry-Tailored Terminology: The corpus encompasses a comprehensive lexicon of Shopping-specific terminology, ranging from technical terms related to e-commerce, product descriptions, and payment processing to customer service and retail operations.

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Authentic Industry Expressions: Beyond technical terminology, the corpus captures the authentic expressions, idioms, and colloquialisms used within the Shopping domain.

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Contexts Specific to Shopping Domain: The corpus encompasses various shopping-related contexts, including product information such as descriptions and specifications, customer feedback in the form of reviews and ratings, transactional messages like payment confirmation and transaction updates, navigation and exploration aids like category descriptions and subcategory details, marketing materials including promotions, advertisements, and discounts, order management updates like tracking and shipping notifications, return and exchange policies and procedures, and customer support resources like FAQs and support sections.

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Cross-Domain Applicability: While the corpus is specifically designed for the Shopping domain, it also includes relevant terminology and language from related areas, such as Fashion, Beauty, Electronics, Gadgets, etc

Format and Structure

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Multiple Formats: Available in Excel format, with the ability to convert to JSON, TMX, XML, XLIFF, XLS, and other industry-standard formats, facilitating ease of use and integration.

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Structure: It contains information like Serial Number, Unique ID, Source Sentence, Source Sentence Word Count, Target Sentence, and Target Sentence Word Count.

Usage and Application

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Machine Translation: Develop accurate machine translation engines for shopping content localization, enabling seamless shopping experiences across languages.

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NLP Applications: Enabling the creation and improvement of predictive keyboards, spell checkers, grammar checkers, and text/speech understanding systems.

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Language Modeling: Train language models to generate product descriptions, reviews, and other shopping-related content.

Secure and Ethical Collection

•Our proprietary parallel corpus platform “Yugo” was used throughout the process of this dataset creation.

•Throughout the dataset creation process, the data remained within our secure platform and did not leave our environment, ensuring data security and confidentiality.

•It does not include any personally identifiable information, which makes the dataset safe to use.

•The source or translated content included in the corpus does not infringe upon any copyrights or intellectual property rights. The corpus comprises original content created specifically for this purpose.

Update and Customization

To ensure the continued relevance and effectiveness of this Shopping Domain Parallel Corpora Dataset for robust language models and machine translation engines, we are committed to regular updates.

•Customization & Custom Collection Options:

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Annotation: Various types of annotations like Part-of-speech tagging, Named Entity Recognition (NER), Sentiment Analysis, Intent Classification, Multiple Translation Ranking, or any other application-specific annotations can be made available upon request.

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Classification: Classification of corpus based on type of sentence, and subdomain can be made available.

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Custom Collection: Custom collection can be done on specific requirements in any language pair and domain.

License

This Tamil-English Parallel Corpus dataset for the Shopping domain is created by FutureBeeAI and is available for commercial use.

Use Cases

MT Engine

Language model

Predictive keyboards

Spell check

Grammar correction

Use of parallel corpus dataset in Text/speech system

Text/speech systems

Dataset Sample(s)

SAMPLE

SOURCE LANGUAGE

TARGET LANGUAGE

Even with diapers, we have many choices when we go shopping.

ஷாப்பிங் செல்லும்போது, டயப்பர்களில் கூட பல தேர்வுகள் உள்ளன.

Before you go shopping, decide on a budget.

ஷாப்பிங் செல்வதற்கு முன், நீங்கள் பட்ஜெட்டை முடிவு செய்ய வேண்டும்.

She skipped lunch in order to go shopping.

ஷாப்பிங் செல்வதற்காக மதிய உணவை அவள் தவிர்த்துள்ளாள்.

It is best to have your boy with you when shopping.

ஷாப்பிங் செய்யும்போது உங்களுக்கான பையனை உங்களுடன் வைத்துக் கொள்வது நல்லது.

If you are shopping online, check the description before you order.

நீங்கள் ஆன்லைனில் ஷாப்பிங் செய்கிறீர்கள் என்றால், ஆர்டர் செய்வதற்கு முன் விளக்கத்தைச் சரிபார்க்க வேண்டும்.

When shopping for a crib, many parents end up buying convertible cribs.

தொட்டிலுக்காக ஷாப்பிங் செய்யும்போது, பல பெற்றோர்கள் மாற்றத்தக்க தொட்டில்களையே வாங்க விரும்புகிறார்கள்.

The shopping centre is about three-quarters of mile away.

ஷாப்பிங் சென்டர் இங்கிருந்து முக்கால் மைல் தொலைவில் உள்ளது.

Targetojects like out-of-town shopping malls are difficult to develop.

வெளியூர் வணிக வளாகங்கள் போன்ற திட்டங்களை உருவாக்குவது கடினமான ஒன்று.

Shopping for wedding gowns online has never been easier!

திருமண ஆடைகளை ஆன்லைனில் ஷாப்பிங் செய்வது எளிதானது இல்லை!

Always use a reusable shopping bag.

மீண்டும் பயன்படுத்தக்கூடிய வகையிலான ஷாப்பிங் பையை எப்போதும் பயன்படுத்தவும்.

ATTRIBUTES

Source Language :Tamil

Target Language :English

Domain :Shopping

Dataset Details

Dataset type

Text Corpus Data

Volume

50K+ Sentences

Media type

Text

Language pair

English-Tamil

File Details

Type

Bilingual

Word count

7 to 12 words per asset

Format

XLSX, TMX, XML, XLIFF, XLS

Annotation

Read the License Terms

Browse FAQs

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