Data Augmentation

Augmentation

Modifying word vectors to enhance fairness is a good strategy. However, its application is restricted to static vectors. What if we leverage contextual embeddings like BERT? Or refrain from using vectors altogether? We dig into a method that is universally applicable.

Data augmentation involves generating additional training examples. These examples aim to diversify the training data, assisting the model in discerning more robust relationships. We might wonder: by deriving augmented data from the training set, do we genuinely enrich the information available to the model? The ...