Glossary

Dataset cleaning

Dataset cleaning is the process of identifying and correcting errors, inconsistencies or irrelevant information in datasets to improve their quality, accuracy and usability. It ensures that data used to train Artificial Intelligence (AI) or machine learning models is reliable and representative of real-world inputs.

Description

High-quality AI models rely on clean, structured data. Dataset cleaning (sometimes called data cleansing or data preprocessing) involves reviewing and refining raw data before it’s used for model training. This can include removing duplicates, fixing formatting errors, correcting mislabeled data and standardizing linguistic or metadata structures.

In multilingual AI projects – such as speech recognition, machine translation (MT) or chatbot training – dataset cleaning is crucial to eliminate bias and noise that could reduce model performance. Clean datasets produce more accurate and fair AI outcomes, reducing the need for retraining and post-editing later in the process.

At RWS, dataset cleaning is a key step within the TrainAI pipeline. Human linguists and data specialists combine expertise with intelligent automation to ensure data quality, security and diversity. This Human + Technology approach enables organizations to develop scalable, ethical and high-performing AI solutions.

Related terms

Data annotation Data labeling Large language model training Machine translation (MT) TrainAI

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