Quality Assurance Policy

Introduction

At Lingvanex, our Quality Assurance Plan is a fundamental part of our project management strategy, ensuring that our Lingvanex software based on AI Enterprise Translation meets established requirements, objectives, and standards while being delivered to the customers. This process is designed to minimize the risk of defects, delays, and cost overruns, preventing potential project disruptions and ensuring customer satisfaction. By implementing a robust quality assurance plan, we provide stakeholders and customers with confidence that our translations are accurate, reliable, and of the highest quality. This policy outlines the structured approach we take to achieve and maintain these quality standards in our solutions, ensuring that the final product consistently meets or exceeds their expectations.

Objectives

• Deliver high-quality translations that meet or exceed international industry standards.
• Achieve results from language models quality tests that fit or exceed industry quality standards on the COMET and BLEU metrics for all language pairs.
• Ensure the solution is reliable, scalable, and easy to integrate into client applications.
• Minimize defects and ensure timely resolution of issues.

Stakeholders and Roles

• Project Manager. Oversee project execution, manage timelines and resources, ensure communication among stakeholders.
• Development Team. Develop the translation solution, implement features, fix bugs, and optimize performance.
• Quality Assurance Team. Conduct testing, monitor quality metrics, identify and resolve defects, and ensure compliance with quality standards.
• ML Team. Train and fine-tune ML language models, evaluate model performance, and implement improvements.
• Linguistic Team. Compile and validate test datasets, evaluate model outputs, classify and analyze errors, provide expertise on language accuracy, and support model evaluation.
• Customers/Clients. Provide requirements, feedback, and validate the solution.

Quality Assurance Processes and Procedures for ML Language Model Training

Requirements Gathering

• Model Purpose. What specific task should the model perform?
• Data Requirements. Type, volume, and quality of training data needed.
• Evaluation Metrics. How will model success be measured (e.g., BLEU score, human evaluation)
• Ethical Considerations. Identify potential biases in the data and ensure the model's outputs are fair and unbiased.

Development

• Agile Methodology. Break down the training process into smaller, iterative cycles.
• Continuous Integration. Regularly integrate and test code changes.
• Version Control. Track changes in model architecture and training parameters.

Testing

• Data Validation. Ensure data is clean, formatted correctly, and free of errors.
• Code Testing. Verify code for errors that could impact training stability or convergence.
• Integration Testing. Ensure different components of the solution work together seamlessly.

System Testing (Model Evaluation)

• Evaluate model performance against predefined metrics using held-out test data.
• Analyze outputs for potential biases or errors.
• Ensure that the models do not exceed the size of approximately 184 MB for better performance.

Acceptance Testing

• Involve human experts (Linguistic Team) to evaluate model outputs for fluency, accuracy, and alignment with requirements.
• Linguistic Team assess the quality of translations by annotating test results, identifying which configurations produce correct translations and highlighting iterations with successful or poor translations. This may lead to additional training or adjustments to the settings.

Performance Testing

• Assess model performance under various data loads and real-world conditions.
• Benchmark against alternative models, if applicable.

Regression Testing

• Retrain the model on updated data and re-evaluate performance to ensure no degradation.
• Monitor model performance in production to detect any drift over time.

Defect Management

• Data Quality Problems. Issues such as missing values or inconsistencies in the data.
• Training Errors. Challenges like convergence issues or overfitting during model training.
• Model Output Deficiencies. Problems with output, including factually incorrect results or bias.
• Error Analysis. Linguists analyze translations for errors, classify these errors, and, where possible, identify their root causes. The technical team then uses this information to make corrections, which are tested to verify if the problem persists or has been resolved.

Approval Processes

• Data Quality. Before training begins.
• Model Performance. During development iterations.
• Final Model. Before deployment.

Quality Metrics and Key Performance Indicators

Quality Metrics

• We evaluate the quality of our models by computing metrics on the flores200 and NTREX-128 test datasets and using our own test datasets compiled by the linguist team.

Key Performance Indicators (KPIs)

• Customer Satisfaction. Measure through surveys and feedback.
• Uptime and Reliability. Monitor system uptime and reliability metrics.
• Scalability. Evaluate system performance under increasing load conditions.
• Integration Success Rate. Percentage of successful integrations with client applications.

Updates of the Quality Assurance Plan

Regular Reviews

• Schedule periodic reviews of the quality assurance plan.
• Analyze quality metrics and KPIs to identify areas for improvement.
• Update processes, procedures, and documentation based on review findings.

Continuous Improvement

• Foster a culture of continuous improvement.
• Encourage feedback from all stakeholders and incorporate it into the quality assurance process.
• Implement best practices and lessons learned from past projects.