Malay General Conversation Dataset

Project Overview:

Objective

Our project, “Malay General Conversation Dataset,” is designed to develop a robust dataset for training machine learning models in natural language processing, specifically focused on the Malay language. This dataset will be pivotal in enhancing technologies like chatbots, voice assistants, and automated translation services.

Scope

The project encompasses the collection and annotation of Malay language conversations from diverse sources. This includes dialogues from native speakers, public domain resources, and scripted scenarios to ensure a rich variety of conversational contexts.

Malay General Conversation Dataset
Malay General Conversation Dataset
Malay General Conversation Dataset
Malay General Conversation Dataset

Sources

  • Native Malay Speakers: Engaging with individuals from different regions to capture dialectical variations.
  • Public Domain Resources: Utilizing available Malay language datasets to enrich our collection.
  • Scripted Conversations: Creating controlled conversational scenarios to cover a wide range of topics and situations.
Malay General Conversation Dataset
Malay General Conversation Dataset

Data Collection Metrics

  • Total Conversations Collected: 20,000
  • Native Speakers’ Contributions: 12,000
  • Public Domain Conversations: 5,000
  • Scripted Dialogues: 3,000

Annotation Process

Stages

  1. Conversation Categorization: Each conversation is annotated with tags indicating the context, subject, and conversational style.
  2. Metadata Annotation: Annotating each conversation with metadata such as date, length, and speaker demographics.

Annotation Metrics

  • Conversations with Contextual Labels: 20,000
  • Metadata Annotated Conversations: 15,000
Malay General Conversation Dataset
Malay General Conversation Dataset
Malay General Conversation Dataset
Malay General Conversation Dataset

Quality Assurance

Stages

Annotation Verification: Implementing a review process to ensure the accuracy and relevance of annotations.
Data Quality Control: Removing any irrelevant or low-quality conversation samples.
Data Security and Privacy Compliance: Ensuring the protection of participant data and adherence to privacy regulations.

QA Metrics

  • Verified Annotations: 18,000
  • Data Cleansing: Ongoing removal of unsuitable data

Conclusion

Our “Malay General Conversation Dataset” is a comprehensive and high-quality resource, crucial for advancing machine learning models in understanding and processing the Malay language. This dataset not only supports technological advancements in natural language processing but also contributes significantly to the development of culturally and linguistically inclusive AI technologies.

quality dataset

Quality Data Creation

Guaranteed TAT​

Guaranteed TAT

ISO 9001:2015, ISO/IEC 27001:2013 Certified​

ISO 9001:2015, ISO/IEC 27001:2013 Certified

HIPAA Compliance​

HIPAA Compliance

GDPR Compliance​

GDPR Compliance

Compliance and Security​

Compliance and Security

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