Transfer Learning from Spanish to Portuguese: How Neighbors on the Map Also Share Vectors

Transfer Learning is a technique in natural language processing where a model trained on one task is used for another task by changing its training data. It started with word vectors, which are static vectors representing the relationship between words in a large abstract space. Contextual word vectors like ELMo and transformers improved this process by considering complex characteristics of word use and how they vary across linguistic contexts. Transfer learning is valuable as it allows for better representation of corpus words, improving language-related tasks such as speech recognition. Deepgram highly values transfer learning and uses it to train models for new languages by leveraging knowledge from similar languages' models.