Today, RNNs (for example, LSTM) have been used in a variety of different applications ranging from time series data modeling, image classification, and video captioning, as well as textual analysis. In this section, we will cover some important applications of RNNs for solving different natural language understanding problems.
Applications
Language modeling
Language modeling is one of the fundamental problems in natural language understanding (NLU). The core idea of a language model is to model important distributional properties of the words in a given language. Once such a model is learnt, it can be applied to a sequence of new words to generate the most likely next word token given the learned distributional representation...
