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.
Category
Parallel Corpora
Volume
50K+ Corpus
Last Updated
June 2022
Number of participants
200+ people
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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
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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.
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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
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
NA
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