Natural_Language_Generation-01

28

Nov

Data Annotation for Natural Language Generation Models

Natural Language Generation aka NLG models are designed to generate human-like text and are trained on vast datasets. They have become integral to various applications, from chatbots and virtual assistants to content generation and data summarization.  Data annotation in the context of NLG involves labeling or marking data to provide context, structure, and meaning to the training […]

Crowdsourcing_Data_Annotation-01

18

Nov

Crowdsourcing Data Annotation: Pros, Cons, and Best Practices

Data annotation is a critical step for artificial intelligence’s development as it involves labeling and tagging data to train algorithms. Among its types, crowdsourcing data annotation is an essential variety, which involves outsourcing the labeling task to a large group of contributors instead of hiring fixed annotators. Crowdsourcing data annotation has gained popularity as it is cost-effective[…]

Data_Annotation_Efficiency-01

15

Nov

Transfer Learning for Data Annotation Efficiency

Transfer learning is a machine learning data annotation technique where an AI model trained on one task is adapted for a somewhat related task. In simple words, instead of starting from scratch, using a pre-trained model as a starting point and fine-tuning it as per the specific task is transfer learning.  This concept has been utilized by[…]