The need for large-scale training datasets in NLP
NLP models require large-scale training datasets to perform well in practice. In this section, you will understand why NLP models need a substantial amount of training data to converge.
ML models in general required a huge number of training samples to cover in practice. NLP models require even more training data compared to other ML fields. There are many reasons for that. Next, let’s discuss the main ones, which are as follows:
- Human language complexity
- Contextual dependence
- Generalization
Human language complexity
Recent research shows that a huge proportion of our brains is used for language understanding. At the same time, it is still a research problem to understand how different brain regions communicate with each other while reading, writing, or carrying out other language-related activities. For more information, please refer to A review and synthesis of the first 20years of PET and fMRI...
