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English to Malayalam Education Domain Parallel Corpora

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

Volume

50K+ Corpus

Last Updated

June 2022

Number of participants

200+ people

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Education domain parallel corpora in Malayalam

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About This OTS Dataset

Introduction

Welcome to the English-Malayalam Bilingual Parallel Corpora dataset for the Education domain! This comprehensive dataset contains a vast collection of bilingual text data, carefully translated between English to Malayalam, to support the development of Education-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 education 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 Education 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 Malayalam and another portion is translated from Malayalam 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 Education industry.

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Industry-Tailored Terminology: The corpus encompasses a comprehensive lexicon of Education-specific terminology, ranging from technical terms related to pedagogy, curriculum design, and educational technology to teaching methodologies and learning theories.

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Authentic Industry Expressions: Beyond technical terminology, the corpus captures the authentic expressions, idioms, and colloquialisms used within the Education domain, including classroom instructions, academic discussions, and educational feedback.

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Contexts Specific to Education Domain: The corpus encompasses a diverse range of contexts specific to the Education domain, including lesson plans, academic papers, educational resources, and online courses.

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Cross-Domain Applicability: While the primary focus is on the Education domain, the corpus also includes relevant cross-domain content from related areas, such as child psychology, educational psychology, cognitive science, and learning technologies.

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 educational content

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NLP Applications: Improve predictive keyboards, spell checkers, grammar checkers, and text/speech understanding systems tailored for educational contexts.

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LLM Training: Enhance bilingual capabilities of Large Language Models for educational purposes, such as generating educational content, summarizing texts, and answering questions.

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 Education 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.