Developing a pipeline for semi-automatic annotation with transfer learning

The quality of machine learning and deep learning models depends on the quality of data they are trained on. Thus, data annotation has been an essential task in many ML studies, especially when the goal is to analyze data with subjective labels, such as dialogue topics or emotions of a person. Manual annotation of such subjective data is costly. Recent development in transfer learning and few-shot learning has enabled a more efficient approach of semi-automatic annotation, in which a classification model can be trained with a small amount of manually labelled data and generate reasonably accurate labels for the remaining dataset. The goal of this project is to develop a semi-automatic annotation pipeline using transfer learning.